DEV Community: Bold BI by Syncfusion

What's New in v16.1.90 for Self-Service & Embedded Analytics

Bold BI by Syncfusion — Wed, 15 Jul 2026 13:59:55 +0000

TL;DR: Bold BI® v16.1.90 introduces Anthropic Claude BYOK support, smarter AI-generated insights with currency-aware formatting, faster dashboard rendering, enhanced dashboard navigation, expanded duration formatting options, stronger Keycloak authentication support, and numerous reliability, performance, and usability improvements across the platform.

Introduction

We're excited to announce Bold BI v16.1.90, a release focused on giving organizations greater flexibility in AI adoption, improving dashboard performance, and delivering a more streamlined analytics experience. This release introduces Bring Your Own Key (BYOK) support for Anthropic Claude models, allowing organizations to securely integrate Claude using their own API credentials. In addition, AI-powered analytics are now more reliable with enhanced summary generation and currency-aware insight formatting that improves the readability of financial data.

We've also enhanced the dashboard experience with improved navigation, faster rendering performance, expanded duration formatting options, and stronger authentication support through Keycloak OpenID enhancements.

Alongside these updates, numerous improvements and bug fixes across Designer, Data Sources, Data Hub, Server, and IDP modules help create a more stable, scalable, and dependable analytics platform.

Whether you're building self-service analytics solutions or embedding analytics into customer-facing applications, Bold BI v16.1.90 helps teams create, manage, and consume analytics more efficiently. Let's take a closer look at what's new.

AI Enhancements

We're continuing to expand AI flexibility and model choice, giving organizations greater control over how AI is integrated into analytics workflows.

Bring Your Own Key (BYOK) Support for Anthropic Claude Models

Every organization has unique requirements for AI adoption, security, governance, and compliance. To provide more flexibility, Bold BI now extends its Bring Your Own Key (BYOK) capability to support Anthropic Claude models.

With this enhancement, users can configure and access Claude models using their own Anthropic API credentials directly within Bold BI. Organizations retain full ownership of API usage, billing, and governance policies while taking advantage of Anthropic's AI capabilities.

By expanding support beyond existing AI providers, Bold BI enables organizations to choose the models that best fit their business goals while maintaining compliance with internal security and data governance standards.

BYOK Support for Anthropic Claude Models

Why it matters: Teams can choose the AI provider that best aligns with their security, governance, and operational requirements without sacrificing control over API usage or costs.

Improvements

This release includes enhancements designed to improve AI-generated insights, dashboard accessibility, performance, and authentication reliability.

Smarter Currency Formatting for AI-Generated Insights

Financial insights are most useful when they are easy to understand. Previously, monetary values generated by AI could appear without consistent currency formatting, making financial analysis more difficult, especially in environments that work with multiple currencies.

Bold BI now introduces currency-aware formatting for AI-generated insights. Monetary values are automatically displayed using the appropriate currency symbols and standardized number formats, creating more consistent and readable AI-generated narratives and summaries.

Whether analyzing revenue, costs, profits, or other financial metrics, users can interpret results more quickly and confidently.

Why it matters: Financial figures are easier to read and validate, enabling users to focus on decision-making instead of interpreting data formats

Enhanced Dashboard Navigation Experience

Finding frequently used dashboards is now faster and more intuitive.

The Dashboard Listing page has been enhanced with:

Recent Dashboards for quick access to recently viewed content.
Favorite Dashboards for keeping important dashboards readily available.
Compact and Card view options within Quick Access.
Improved Category Tree alignment.
Child dashboard counts displayed within parent categories.

These enhancements make it easier to browse and manage large dashboard collections while reducing the time required to locate critical analytics resources.

Why it matters: Users can spend less time searching for dashboards and more time focusing on analysis and decision-making.

Faster Dashboard Rendering Performance

Dashboard responsiveness plays a critical role in the analytics experience. To improve loading performance, Bold BI now optimizes dashboard rendering by intelligently grouping widgets that share the same data source.

This approach reduces redundant data retrieval requests and allows shared-source widgets to be processed more efficiently during dashboard loading.

Why it matters: Fewer duplicate data requests lead to faster dashboard rendering, particularly for dashboards containing many widgets connected to the same source.

Extended Duration Format Support

The Duration Format feature has been expanded to support customizable fixed-duration formats, including Year-Month-Day representations.

This enhancement provides dashboard authors with greater flexibility when displaying time-based metrics such as project timelines, service durations, employee tenure, and other elapsed-time measurements.

Duration Format Support

Why it matters: Organizations can present duration-based metrics in formats that better match reporting requirements and business expectations.

Keycloak SSO Token Storage Support for OpenID Provider

Organizations that use Keycloak for authentication can now benefit from enhanced token storage support within OpenID-based single sign-on workflows.

This improvement strengthens session management and authentication reliability, helping users experience smoother sign-in and sign-out processes while improving the stability of identity management integrations.

Why it matters: Organizations running Keycloak-based SSO deployments can deliver more reliable authentication experiences and reduce session-related issues.

Other Bug Fixes and Minor Improvements

In addition to the major enhancements, Bold BI v16.1.90 includes numerous fixes and optimizations across AI, Designer, Data Sources, Data Hub, Server, and IDP modules

Reliability: Improved platform stability through fixes related to:

AI-generated visualizations, dashboard summary generation, and widget insight generation.
Dashboard filtering, chart rendering, and widget configurations.
Dashboard migration, Excel exports, and data previews.
Group management, authentication workflows, and Data Hub connectivity.

Performance: Enhanced dashboard loading and data retrieval through:

Dashboard rendering optimizations.
Elasticsearch connector improvements, including API optimizations, retry mechanisms, and query metrics processing.

Compatibility: Addressed issues across:

PostgreSQL, Elasticsearch, Trino, and Microsoft SQL Server.
Azure Active Directory, OpenID providers, and Keycloak integrations.
Data Hub Web Connectors, cloud deployments, and dashboard migration scenarios.

Usability: Improved the dashboard authoring and viewing experience through fixes related to:

Column Charts, Period-over-Period widgets, and Pie and Doughnut charts.
Date Pickers, Range Sliders, Number Cards, Pivot Grids, and Tab Widgets.
Modern Widget Panel components, calculated expressions, map interactions, and dashboard filtering workflows.

Real-World Use Cases Enabled by Bold BI v16.1.90

The Bold BI v16.1.90 release focuses on helping organizations improve AI flexibility, dashboard usability, analytics performance, and enterprise authentication. Here's how these enhancements translate into real business value.

Adopt AI While Maintaining Control Over Provider Credentials

Challenge: Organizations want to adopt AI-powered analytics without giving up ownership of API credentials, usage policies, and governance controls.
Bold BI solution: Anthropic Claude BYOK support enables teams to connect and use Claude models with their own Anthropic API credentials.
Business impact: Organizations gain greater flexibility in AI provider selection while maintaining control over compliance, billing, security, and governance policies.

Improve Understanding of Financial Insights

Challenge: Inconsistent presentation of currency values can make AI-generated financial insights difficult to interpret.
Bold BI solution: Currency-aware formatting automatically applies appropriate symbols and standardized formatting to AI-generated monetary values.
Business impact: Users can understand financial metrics more quickly and make decisions with greater confidence.

Help Users Find Dashboards Faster

Challenge: Large analytics environments can make dashboard discovery time-consuming.
Bold BI solution: Recent Dashboards, Favorite Dashboards, enhanced Quick Access options, and improved Category Tree navigation simplify dashboard browsing.
Business impact: Users can reach important business insights faster, improving productivity and reducing navigation overhead.

Accelerate Analytics Experiences for Large Dashboards

Challenge: Dashboards containing multiple widgets can experience longer load times when data is retrieved repeatedly from the same source.
Bold BI solution: Intelligent widget grouping reduces redundant data retrieval operations during dashboard rendering.
Impact: Faster dashboard loading improves user experience and helps organizations deliver responsive analytics at scale.

Simplify Analytics for Duration-Based Metrics

Challenge: Organizations often need flexible ways to represent project timelines, service durations, employee tenure, and other time-based measures.
Bold BI solution: Enhanced Duration Format support introduces customizable formats, including Year-Month-Day representations.
Impact: Teams can present duration-based data in a format that aligns with business reporting requirements and improves readability.

Strengthen Enterprise Authentication Workflows

Challenge: Large organizations require reliable authentication and seamless single sign-on experiences across analytics deployments.
Bold BI solution: Keycloak OpenID token storage enhancements, Azure AD synchronization fixes, OpenID logout improvements, and user-management stability updates strengthen authentication workflows.
Impact: Administrators can reduce authentication-related issues while providing users with a more reliable and secure sign-in experience.

Conclusion

Bold BI® v16.1.90 delivers meaningful improvements across AI-powered analytics, dashboard performance, navigation, authentication, and platform reliability. From Anthropic Claude BYOK support and currency-aware AI insights to enhanced dashboard navigation, faster dashboard rendering, advanced duration formatting, and improved Keycloak authentication support, this release helps organizations build, manage, and consume analytics more effectively.

Ready to explore what's new? Existing customers can download Bold BI v16.1.90 today and new users can start a free 30-day trial to experience the latest enhancements in self-service and embedded analytics. For a complete list of features, improvements, and fixes included in this release, visit our release history page. If you have any questions or need assistance, contact our support team for help.

How to Embed Dashboards in React with a Python Backend

Bold BI by Syncfusion — Wed, 15 Jul 2026 13:40:07 +0000

TL;DR: Embedding Bold BI® dashboards in a React application with a Python backend enables secure, scalable, and seamless analytics integration. The Python backend handles token-based authentication, while React delivers a responsive embedded experience with routing and component-based rendering.

Introduction

Modern applications are evolving beyond basic functionality to deliver data-driven experiences directly within the product interface. Users expect real-time insights without switching between tools, making embedded analytics a core feature in today's software landscape.

To meet this expectation, developers need an approach that ensures both seamless user experience and strong security.

In this guide, you'll learn how to embed Bold BI dashboards into a React application using a Python backend (Django), following the latest Embed SDK approach. More importantly, we'll explain why each step matters, so you can confidently implement this in real-world applications.

Before you start: Bold BI's getting started guide covers the core embedding concepts and is worth a quick read before you dive into the sample below.

Why Embed Dashboards in React Applications

React's component-driven architecture allows developers to integrate dashboards as part of a unified interface, rather than treating analytics as a separate system.

This creates a more connected product experience. As a result, you can:

Deliver a consistent and seamless user experience.
Provide real-time insights within workflows.
Increase product engagement through interactivity.
Differentiate your application with built-in analytics.

Why a Python Backend Is Essential

When embedding dashboards, security is non-negotiable. Since dashboards often contain sensitive data, authentication should never be handled on the frontend.

A Python backend, commonly implemented using Django, is responsible for:

Generating secure embed tokens.
Managing authentication and user authorization.
Protecting secret keys and credentials.
Acting as a secure bridge to the Bold BI server.

By keeping these operations on the server side, no sensitive information is exposed in the browser. This backend-first approach establishes a secure and scalable architecture that’s suitable for production environments.

How to Embed a Dashboard in a React Application with Python

The following steps demonstrate how to configure embedding, set up the required components, and render dashboards in a React application using a Python backend and the Bold BI Embedded SDK.

Prerequisites

Before you begin, ensure the following requirements are met:

Python.
Node.js and npm (required to install and run the React app).
Visual Studio Code.
Python Extension for Visual Studio Code.

Step 1: Get the React with Python Sample

Start by cloning the official React with Python sample application from the Bold BI GitHub repository:

git clone https://github.com/boldbi/react-with-python.git

This sample demonstrates:

Rendering a single dashboard.
Rendering a list of dashboards.
Embedding Bold BI dashboards using React and Python.

Step 2: Embed Authentication in Bold BI

Before embedding a dashboard, Embed Authentication must be enabled.

To enable embedding:

Log in to your Bold BI instance.
Go to Administration.
Navigate to Embed Settings.
Enable Embed Authentication.
Save the changes.

This step generates the Embed Secret needed for secure dashboard access.

Step 3: Download the Embed Configuration file

After enabling embed authentication:

Go to the dashboard you want to embed.
Open the Embed tab.
Click Embed Configuration.
Download the embedConfig.json file.

The downloaded file contains all the required configuration details for embedding.

Step 4: Add the Embed Configuration file

Copy the downloaded embedConfig.json file and place it in the application location shown in the sample project structure.

The file contains the following settings:

Property	Description
ServerUrl	The URL of your Bold BI server. Example: http://localhost:5000/bi or https://demo.boldbi.com/bi
SiteIdentifier	Enterprise Edition: site/site1; Bold BI Cloud: empty string
Environment	BoldBI.Environment.Cloud or BoldBI.Environment.Enterprise
DashboardId	The dashboard item ID to be embedded
EmbedSecret	The embed secret generated after enabling embed authentication
UserEmail	Admin user email address used for dashboard access
ExpirationTime	Token expiration time in seconds. Default: 10,000 seconds

Step 5: Start the Python Backend

Navigate to the Python project and run:

python manage.py runserver

The Python application will start on:

http://localhost:8000

The backend is responsible for generating secure authentication tokens required by the Embedded SDK.

Step 6: Install React Dependencies

Move to the React application folder and install all required packages:

npm install

Step 7: Start the React Application

After package installation completes, run:

npm start

The React application will launch on:

http://localhost:3000

and connect to the Python backend to securely retrieve dashboard tokens.

Step 8: View Dashboard Listing (Optional)

By default, the sample displays a single embedded dashboard.

To view the dashboard listing page, navigate to:

http://localhost:3000/dashboardlisting

This page displays the available dashboards in a sidebar for selection.

For more guidance on integrating dashboards into a React application, refer to our help documentation.

How the Sample Works

1. Dashboard Initialization in React

The React application uses BoldBI.create() to initialize the embedded dashboard. The configuration includes:

Dashboard information.
Server information.
Token generation endpoint URL.

The tokenGenerationUrl property points to the Python backend's tokenGeneration endpoint.

2. Token Generation

The Python backend exposes a token generation API. It:

Receives requests from React.
Communicates with the Bold BI server.
Retrieves an authentication token.
Returns the token to the React application.

This ensures the Embed Secret is never exposed in the browser.

3. Dashboard Rendering

Once the token is generated, it's returned to Dashboard.js, Bold BI validates it, and the dashboard renders inside the React application. Users can then interact with the dashboard directly from the embedded page.

Verify the Dashboard

Confirm that:

The Python backend is running on port 8000.
The React application is running successfully.
The dashboard renders inside the React application.
Authentication occurs securely through the Python backend.

Best Practices for Production Deployment

After successfully embedding dashboards, the next step is to ensure your implementation is production-ready.

Secure token handling: Always generate tokens on the backend, use HTTPS, and set expiration limits.
Multi-tenant architecture: Assign roles and restrict access to ensure users only see relevant data.
Performance optimization: Use caching and optimize queries to improve load times.
UI and UX consistency: Make dashboards match your application's design and ensure responsiveness.
Monitoring and logging: Track usage and performance to detect and resolve issues early.

By following these practices, you ensure a stable, scalable, and secure analytics experience.

Why Choose Bold BI for Embedding Dashboards in a React App with Python?

Bold BI® enables secure analytics embedding in React applications using Python for token generation and authentication. With interactive dashboards, the Embedded SDK, and server-side authentication, developers can quickly integrate analytics into their applications.

Bold BI allows users to:

Embed dashboards directly into React workflows.
Access the dashboard securely with Python token generation.
Deliver interactive analytics experiences.
Simplify integration using the Embedded SDK.
Scale analytics across users and applications.
Support multi-tenant SaaS analytics.

Whether you are building SaaS platforms, enterprise applications, or customer portals, Bold BI provides a secure and scalable way to embed analytics into your React applications with Python.

Ready to get started? Download the React with Python sample, configure your embedding settings, and start delivering interactive dashboards. To learn more, explore the documentation, start a free trial, or request a demo to see how Bold BI fits your use case.

Frequently Asked Questions

1.
Why use Django for embedding dashboards?
Django securely generates embed tokens and manages backend authentication.
2.
Can I embed dashboards without a backend?
For secure token-based embedding, a backend should generate tokens and protect secrets.
3.
What is tokenGenerationUrl?
It is the backend API endpoint used by the SDK to generate secure embed tokens.
4.
Does Bold BI support React integration?
Yes, through its latest Embed SDK.
5.
Is this architecture scalable?
Yes, it supports enterprise and multi-tenant deployments.

Conversational Analytics in BI: Why It Matters in 2026

Bold BI by Syncfusion — Thu, 09 Jul 2026 08:23:22 +0000

TL;DR: Conversational analytics means users ask data questions in natural language and get answers fast. It reduces routine dashboard navigation and analyst dependency while making BI easier to use across teams.

Introduction

In many BI workflows, users still need to search through dashboards, apply filters, or wait for analysts to answer routine questions about their business data.

A dashboard may show the right metrics, but it does not always answer the question a business user has. Sales teams may want to understand why revenue dropped. Finance teams may need a quick variance explanation. Operations teams may want to identify what has changed since the last analytics period.

That is why conversational analytics matters. It enables users to ask questions in natural language, receive relevant insights, and continue exploring data through follow-up questions. With AI and natural language processing, conversational analytics can help teams find answers more efficiently while allowing analysts to focus on more complex analysis.

What is conversational analytics?

Conversational analytics is an approach that allows users to interact with data using natural language. Instead of writing traditional queries or navigating dashboards, users can ask questions such as:

“How many orders did we place in China last year, by month?”
“Which region has the highest growth?”

The system interprets the request and returns:

Visualizations
Data summaries
Insights

This makes analytics accessible to a broader range of business leaders while helping teams explore data more efficiently.

Why organizations are adopting conversational analytics

BI tools often rely on static dashboards, predefined queries, and technical expertise, making it harder to adapt to dynamic business questions and quickly find answers.

Conversational analytics helps address these challenges by enabling organizations to:

Accelerate business decision-making: Users can ask questions in natural language and receive timely insights, helping teams respond more quickly to changing business conditions.
Expand self-service analytics: Business leaders can independently explore data through conversational interactions, reducing routine requests to analysts.
Improve productivity across teams: Routine business questions can be answered more efficiently, allowing analysts to focus on strategic analysis, data modeling, and advanced analytics.
Explore data more naturally: Users can refine questions through follow-up conversations, making it easier to investigate trends, understand performance changes, and uncover insights.
Increase analytics adoption: By simplifying how users interact with data, conversational analytics encourages broader BI usage across departments and supports more data-driven decision-making.

Conversational analytics helps organizations access insights more easily and make faster, smarter decisions by overcoming the limitations of traditional BI.

Challenges to consider before adoption

Before implementing conversational analytics, organizations should carefully evaluate potential challenges to ensure successful adoption and maximize business value. Key considerations include:

Data quality issues: Conversational analytics is only as reliable as the data it analyzes. Inaccurate or incomplete data can cause misleading insights.
Security and privacy concerns: Organizations must ensure sensitive data is protected through proper access controls and compliance measures.
AI accuracy and reliability: AI may occasionally misinterpret queries or provide inaccurate results.
User trust and adoption: Employees may be hesitant to rely on AI-generated insights without transparency and validation mechanisms.
Integration complexity: Connecting conversational analytics with existing data sources, BI tools, and business systems can require significant effort.

For the end user, these challenges are invisible: they simply ask questions and get answers more efficiently.

Key features of conversational analytics

Conversational analytics combines AI and natural language technologies to make data exploration more intuitive, interactive, and accessible. Some of its key features include:

Natural language processing (NLP): Enables users to ask questions in plain language and allows the system to understand and interpret human speech or text.
Intent recognition: Identifies the purpose behind a user's query and delivers relevant insights based on the user's goals.
Context awareness: Maintains conversation history, allowing users to ask follow-up questions without repeating previous information.
Sentiment analysis: Detects emotions, opinions, and overall sentiment within conversations to help organizations better understand customer experiences.
AI-powered insights: Goes beyond answering questions by automatically identifying trends, anomalies, patterns, and opportunities within data.
Topic extraction: Identifies recurring themes and discussion topics, helping organizations uncover common issues, interests, and trends.
Data visualization and analytics: Presents data through charts, graphs, dashboards, and visual dashboards that make findings easier to understand and act on.

How conversational analytics differs from traditional BI and NLQ

Conversational analytics goes beyond traditional BI and natural-language querying by enabling context-aware, multistep data exploration through natural conversations, helping users refine questions and uncover deeper insights.

Aspect	Traditional BI	Natural Language Querying (NLQ)	Conversational analytics
Interaction	Dashboards	Single natural language query	Natural language conversations
Scope	Predefined analysis	Single question and response	Continuous exploration through follow-up questions
Context Awareness	Limited	Limited	Context-aware and guided
Accessibility	Often requires BI knowledge	Accessible to business users	Accessible to a broader range of users (with appropriate permissions)
Flexibility	Predefined insights	Limited to the submitted query	Dynamic and iterative exploration
Dependency on Analysts	Higher	Moderate	Reduced
Outcome	Dashboards	Query results	Insights with follow-up exploration

Note: In many BI platforms, NLQ is a feature that enables users to ask individual questions in plain language. Conversational analytics builds on that capability by supporting context-aware, multi-turn interactions that help users explore data through an ongoing dialog.

Real-world use cases of conversational analytics

Conversational analytics is used across industries and business functions.

Sales performance analysis

Sales teams often need quick visibility into pipeline performance and deal trends across regions and time periods. Conversational analytics enables leaders to ask questions like “Why did the pipeline value drop in Q2?” or “Which region underperformed?” and get immediate answers. This helps identify root causes faster, improve forecasting accuracy, and prioritize high-impact opportunities.

Conversational analytics in Sales

Distributor delivery performance analysis

Operations teams often need to monitor distributor performance and track delivery reliability across locations and time periods. Conversational analytics enables users to ask questions like “Which region has the lowest on-time delivery rate?” or “Which distributor handled the highest shipment volume?” and get instant answers. This helps uncover performance gaps, optimize logistics operations, and strengthen supply chain efficiency.

Conversational analytics in Distributor Delivery Performance Dashboard

Financial and invoice analysis

Finance and project management teams often need timely visibility into revenue generation, billable utilization, and client profitability across projects. Conversational analytics enables users to ask questions like “Which clients generated the most revenue?” or “How have billable hours changed over time?” The answers help them monitor financial performance, optimize resource allocation, and improve invoicing accuracy.

Conversational analytics in Finance

How to evaluate conversational analytics platforms

To get the most value, organizations should:

Start with clear business questions: Focus on common use cases like revenue tracking, customer churn, or operational performance.
Ensure high-quality data: Accurate insights depend on clean, well-structured data sources.
Combine with existing BI tools: Conversational analytics works best when paired with dashboards, not as a replacement.
Encourage user adoption: Train teams to ask better questions and explore data actively.

How Bold BI supports conversational analytics

Conversational analytics becomes more valuable when it is built on governed data, understands business terminology, and provides meaningful responses instead of simple keyword matches. Bold BI® supports this experience through AI-powered capabilities that help users explore data using natural language while maintaining enterprise governance.

1. Ask questions in Natural Language with Unified AI Agent

The Unified AI assistant allows users to interact with connected data sources using everyday language instead of manually searching dashboards or writing queries. Users can ask questions such as "How did quarterly sales perform?" or "Compare revenue across regions," and receive contextual responses with supporting visualizations.

The conversational experience also supports follow-up questions, enabling users to continue exploring trends, comparisons, and business metrics without restarting their analysis.

Unified AI Assistant

2. Improve question understanding with AI Synonyms

Organizations often use different business terms to describe the same data. Bold BI allows administrators to configure column and value synonyms so the AI can recognize alternative names, abbreviations, and business-specific terminology.

This improves intent recognition and helps conversational analytics deliver more accurate responses, even when users phrase questions differently.

3. Generate widgets from natural language

Beyond answering questions, Bold BI can transform natural language prompts into visualizations. The AI Copilot automatically recommends appropriate charts based on the requested analysis, making it easier for business users to understand trends, comparisons, and key metrics without manually designing dashboards.

AI Copilot in Bold BI

4. Create dashboards from a single prompt

For broader analysis, the prompt-to-dashboard feature converts a natural language prompt into a fully functional, interactive dashboard. Instead of creating multiple widgets individually, users can describe the information they need, and Bold BI automatically builds a dashboard connected to the relevant data.

This helps accelerate dashboard creation while making analytics more accessible to both technical and nontechnical users.

Prompt to dashboard

5. Explain Insights with AI-Generated Narrations

Bold BI also supports AI-generated narrations that summarize dashboards and widgets in plain language. These explanations help users quickly understand important trends, anomalies, and performance metrics without interpreting every visualization individually.

Combined with conversational analytics, AI-generated narrations make insights easier to find, share, and act upon across business teams.

Smart narration for data clarity

Together, these capabilities help organizations bring conversational analytics into the full analytics lifecycle, from asking questions and exploring insights to creating dashboards and embedding analytics into business applications.

Ready to make conversational analytics easier to explore?

Modern BI teams need more than dashboards. They need a governed analytics experience where users can make natural language inquiries, ask follow-up questions, and understand data with less friction.

With Bold BI®, organizations can utilize conversational analytics through AI capabilities that make data exploration more accessible across teams.

Try Bold BI with a 30-day free trial or request a personalized demo to see how conversational analytics can support your analytics workflow.

Frequently asked questions

1. What is conversational analytics?
Conversational analytics is AI that allows users to interact with data using natural language to quickly get insights.
2. How does conversational analytics work?
It uses NLP and AI to interpret user queries, retrieve relevant data, and generate insights.
3. How is it different from traditional BI?
Traditional BI relies on dashboards, while conversational analytics allows direct interaction with metrics using natural language.
4. What is the difference between conversational analytics and NLQ?
NLQ handles single queries, while conversational analytics supports continuous, context-aware interactions.

Integrate MySQL with Elasticsearch using Bold Data Hub

Bold BI by Syncfusion — Thu, 09 Jul 2026 08:17:45 +0000

TL;DR: Many organizations store transactional data in MySQL while search logs and event data live in Elasticsearch. Analyzing these datasets together is often difficult because they exist in separate systems. With Bold Data Hub, you can integrate MySQL and Elasticsearch into a unified dataset and build interactive dashboards in Bold BI® without writing complex data pipelines.

Introduction

Managing data across multiple systems is a common challenge for developers. Key information often lives in MySQL, while logs, search activity, and system events sit separately in Elasticsearch. Without combining these datasets, teams often rely on manual exports, custom scripts, or complex data pipelines. This is where MySQL and Elasticsearch integration are needed.

By integrating MySQL and Elasticsearch sources, organizations can analyze operational data and search behavior together, creating a complete view of business performance.

In this blog, you’ll learn how to use Bold Data Hub to connect MySQL and Elasticsearch, combine them into a unified dataset, and publish the result in Bold BI for efficient data visualization. Let’s get started.

Benefits of integrating MySQL and Elasticsearch data for analytics

Combining MySQL and Elasticsearch data in one dashboard provides the following benefits:

Better search and transaction analysis: By combining MySQL transactional data with Elasticsearch search logs, teams can analyze how users search for products and how those searches translate into purchases.
Unified analytics across operational and search data: MySQL stores structured business data such as orders and users, while Elasticsearch stores indexed search or event data. Integrating both allows dashboards to correlate operational metrics with user behavior.
Reduced manual data pipelines: Without integration tools, developers often export MySQL data and combine it with Elasticsearch using custom scripts. Bold Data Hub simplifies this by combining both sources in a single pipeline.
Build a performance monitor dashboard: Once the datasets are combined, you can create dashboards with real-time insights without writing custom scripts.

Challenges of integrating MySQL and Elasticsearch

Keeping both systems synchronized becomes difficult as data updates occur, causing inconsistencies between stored and indexed data.
Mapping and schema mismatches arise as structures differ, creating indexing issues and unpredictable search results.
Sync failures happen when pipelines break or delays occur, resulting in missing, outdated, or incomplete records.
Query translation is required because SQL logic doesn’t align with Elasticsearch, increasing development time and complexity.

How Bold Data Hub simplifies data integration

Unified data connectivity brings all your sources together, making it easy to connect databases, files, cloud apps, and APIs from one place.
Automated ETL workflows handle data movement from end to end, ensuring accurate and consistent processing without manual effort.
Powerful data transformation lets you clean, format, and reshape information using clear, customizable rules.
Centralized monitoring and management give full visibility into every pipeline, making performance and data quality easy to track.

Now that you understand how Bold Data Hub simplifies data integration, let’s explore how to connect MySQL and Elasticsearch to the Bold Data Hub.

Integrate MySQL with Elasticsearch data using Bold Data Hub

Bold Data Hub lets you easily combine MySQL and Elasticsearch data in one workspace, enabling unified visualizations and reducing manual preparation.

Step 1: Prepare the data sources

Before integration, ensure that:

MySQL database contains the required tables (for example: user data).
Elasticsearch contains relevant indexes (for example: feedback data).
Both datasets include a common field that can be used as a join key. [caption id="" align="alignnone" width="1484"] Sample data in MySQL[/caption]

[caption id="" align="alignnone" width="1484"] Sample data in MySQL[/caption]

This field allows datasets from MySQL and Elasticsearch to be linked together.

Step 2: Create a data pipeline in Bold Data Hub

Open Bold BI and launch Bold Data Hub. [caption id="" align="alignnone" width="1484"] Click the Bold Data Hub icon[/caption]
Next, select the plus (+) icon to create a pipeline. In this tutorial, the pipeline is named elasticsearch_mysql to represent the integration between Elasticsearch and MySQL. [caption id="" align="alignnone" width="1484"] Create the elasticsearch-mysql pipeline [/caption]
Add the MySQL data and configure it with your host, port, database, and credentials to extract the required tables. Example configuration:

version: 1.0.1

plugins:

extractors:

- name: mysql

connectorname: MySQL

config:

host: localhost

port: 3306

database: sales_db

username: root

password: ********

This configuration extracts transactional data from the MySQL database.

[caption id="" align="alignnone" width="1484"] Extract MySQl data in Bold Data Hub[/caption]

Step 3: Add Elasticsearch as a data source

Next, configure the Elasticsearch connector inside the same pipeline with your host, port, database, and credentials to extract the required tables.

Example configuration:

version: 1.0.1

plugins:

extractors:

name:     elastic_search

connectorname: Elasticsearch

config:

host: http://localhost:9200

index: product_feedback

username: elastic

password: ********

This allows the pipeline to retrieve search or event data stored in Elasticsearch.

[caption id="" align="alignnone" width="1484"] Add Elasticsearch data in Bold Data Hub[/caption]

Step 4: Save the datasets

After combining the MySQL and Elasticsearch datasets in the pipeline, select Save to store the unified configuration.

[caption id="" align="alignnone" width="1484"] Click the save button[/caption]

Once saved, a window will appear displaying the newly combined data source, confirming that the integration has been successfully created and is ready for use in the Data Source page.

[caption id="" align="alignnone" width="1484"] Data source listing page[/caption]

With the data source, you can join the tables from both MySQL and Elasticsearch to unify the information into a single, consistent structure.
Next, we will learn how to perform these joins using tables.

Joining tables

Joining tables allows you to combine information from different sources into a single analytical view. This makes it possible to correlate transaction data stored in MySQL with search or event data stored in Elasticsearch, enabling deeper analysis across systems.
The steps below show you how to create the join.

Step 1: Open the data source editor

Open the action menu for the created data source and select Edit Data Source.

[caption id="" align="alignnone" width="1484"] Edit the data source[/caption]

Step 2: Identify the common fields

The design area opens automatically. Drag and drop the tables from the left panel into the design area. In this case, we dragged products and sales_feedback tables.

[caption id="" align="alignnone" width="1484"] Identify common fields[/caption]

Step 3: Define the join condition

Once you drag and drop the second table into the designer, the query joiner window will open. In the Query Joiner window, define a join using a key.

Example:

mysql.products.product_id = elasticsearch.feedback_id

After configuring the join condition, select Update to apply the changes.

[caption id="" align="alignnone" width="1484"] Query Joiner window[/caption]

Step 4: Preview the combined datasets

Once the join is applied, the design page automatically displays a preview of the merged dataset. This allows you to verify that the data from both sources has been combined correctly.

[caption id="" align="alignnone" width="1484"] Preview of the combined datasets[/caption]

Next, let’s explore the key metrics you can build and visualize in the interactive dashboard.

How to create a dashboard using MySQL and Elasticsearch data

New users can begin by exploring our video on how to create a dashboard in Bold BI, which provides a quick overview of the dashboard-building process.

After connecting both MySQL and Elasticsearch as data sources, the next step is to blend the datasets to define meaningful metrics and KPIs. With the combined data, you can build a dashboard that highlights insights such as:

Total purchases.
Average render time.
Sales payment by type and browser.
Abandoned carts.
Abandoned revenue.
Products sold breakdown.
Page visits by traffic and device type.

These insights help organizations understand both customer intent and business outcomes. With Bold BI, you can easily transform the integrated dataset into interactive dashboards for decision-making.

[caption id="" align="alignnone" width="1484"] Preview of the dashboard[/caption]

Integrating MySQL with Elasticsearch data enables users to enjoy faster searches, improved data analysis, and more efficient access to insights from large datasets. To learn tips for designing effective dashboards, explore our article on 10 dashboard design best practices for insights.

[elementor-template id="150021"]

Start building unified analytics today

Integrating MySQL and Elasticsearch enables organizations to combine structured business data with advanced. With Bold Data Hub, developers can build a unified data pipeline without writing complex ETL workflows. Once the data is integrated, Bold BI dashboards make it easy to explore trends, monitor performance, and share insights across teams.

If you want to simplify MySQL and Elasticsearch integration and build powerful analytics dashboards, try Bold BI® today. Start your 30-day free trial or request a personalized demo to see how Bold BI and Bold Data Hub can help your team turn scattered data into actionable insights.

Frequently asked questions

1.
What data sources does Bold Data Hub support?

Bold Data Hub supports a wide range of data sources, including files, web APIs, SQL databases, and NoSQL systems. This includes popular options such as MySQL and Elasticsearch.
2.
Do I need any technical expertise to use Bold Data Hub?

No advanced technical expertise is required. A basic understanding of the data connectors you plan to use is sufficient to get started.
3.
Does Bold Data Hub offer additional features?

Yes. You can configure multiple datastores of the same server type and load data into them for different projects. It also includes an Open API connector and a Python connector for more advanced use cases.
4.
Do I need Bold Data Hub to use Bold BI?

No. Bold BI can connect directly to many supported data sources. Bold Data Hub is optional and is especially useful when you need to prepare, clean, or combine data, such as blending MySQL and Elasticsearch data, before building dashboards.

Bold BI Wins 23 Badges in the G2 Summer 2026 Awards

Bold BI by Syncfusion — Mon, 06 Jul 2026 08:19:50 +0000

TL;DR: The G2 Summer 2026 Awards recognized Bold BI with 23 badges across Embedded Business Intelligence, Business Intelligence, and Analytics Platform categories. Based on verified customer reviews, Bold BI earned multiple Leader, High Performer, and Momentum Leader distinctions for excellence in usability, implementation, customer satisfaction, and embedded analytics. These recognitions reflect the platform's ability to help organizations deliver scalable, secure, and data-driven analytics experiences.

Introduction

We are excited to announce that Bold BI® has earned 23 badges in the G2 Summer 2026 reports across Embedded Business Intelligence, Business Intelligence, and Analytics Platform categories.

These badges are based on verified G2 reviews and reflect how organizations use Bold BI to build dashboards and embed analytics into their applications. Each badge reflects customer-rated performance in areas such as usability, implementation, and support.

As embedded analytics continue to play a critical role in modern business applications, these achievements reinforce our commitment to delivering meaningful value and helping organizations transform data into actionable insights.

Bold BI Earns G2 Summer 2026 Recognition

Our customers are at the heart of every recognition we receive. Based on verified G2 reviews, Bold BI earned 23 badges in the G2 Summer 2026 reports across Embedded Business Intelligence, Business Intelligence, and Analytics Platform categories.

This recognition reflects customer experiences with Bold BI's usability, implementation, support, and embedded analytics capabilities. From Leader and High Performer distinctions to Momentum Leader recognition, the badges highlight our continued commitment to delivering scalable analytics solutions that help organizations make data-driven decisions with confidence.

Let's take a closer look at the badges Bold BI earned across each category.

Analytics Platforms G2 Badges: Consistent Performance Across Markets

Bold BI delivered strong performance this season, earning recognition across global, regional, and business segments. These badges reflect consistent customer satisfaction and growing adoption across diverse markets.

Here's a breakdown of the badges earned in the Analytics Platform category.

Badge	Recognition
Summer 2026 Americas	High Performer
Summer 2026 Asia	High Performer
Summer 2026 Asia Pacific	High Performer
Summer 2026 Enterprise	High Performer
Summer 2026	High Performer
Summer 2026 Mid-Market	High Performer
Summer 2026 Americas Small Business	High Performer
Summer 2026 Asia Small Business	High Performer
Summer 2026 Asia Pacific Small Business	High Performer
Summer 2026 Small Business	High Performer

Why they matter: These badges highlight Bold BI's ability to deliver consistent value across global and regional markets, as well as enterprise, mid-market, and small business segments.

Business Intelligence G2 Badges: Recognized as a Leader

Bold BI continues to be recognized for delivering intuitive dashboards, flexible reporting, and a seamless analytics experience. Its ability to address diverse business needs has earned recognition across multiple market segments.

Here's a breakdown of the badges earned in the Business Intelligence category.

Badge	Recognition
Summer 2026	Leader
Summer 2026 Mid-Market	Leader
Summer 2026 Small Business	Leader

Why they matter: These badges reinforce Bold BI's position as a trusted analytics solution that delivers consistent value, usability, and performance across different business segments.

Embedded Business Intelligence G2 Badges: Strong Global Presence

Bold BI's embedded analytics capabilities continue to gain global recognition, helping organizations integrate dashboards directly into their applications and workflows. These badges highlight the platform's flexibility, scalability, and performance across various regions and business segments.

Here's a breakdown of the badges earned in the Embedded Business Intelligence category.

Badge	Recognition
Summer 2026 Americas	High Performer
Summer 2026 Asia	High Performer
Summer 2026 Asia Pacific	High Performer
Summer 2026 EMEA	High Performer
Summer 2026 India	High Performer
Summer 2026 Mid-Market	High Performer
Summer 2026 Americas Small Business	High Performer
Summer 2026 Overall	Leader
Summer 2026 Small Business	Leader
Summer 2026 Overall	Momentum Leader

Why they matter: These recognitions demonstrate Bold BI's strength in delivering scalable embedded analytics experiences across industries and regions while continuing to earn customer trust through innovation, performance, and customer satisfaction.

Why Leading Organizations Choose Bold BI

The G2 Summer 2026 recognitions are backed by feedback from customers who use Bold BI to power analytics across their organizations. User reviews consistently highlight several areas where Bold BI delivers value and enables teams to succeed.

Customers appreciate Bold BI for:

Exceptional customer support: Get fast, reliable assistance from a responsive support team that helps organizations navigate implementation, customization, and deployment with confidence.
Ease of use: Create and manage dashboards effortlessly with an intuitive interface that simplifies analytics for both technical and business users.
Flexible data integration: Connect to a wide range of data sources and unify business data to create comprehensive, real-time analytical views.
Advanced dashboard customization: Build tailored analytics experiences using customizable dashboards, widgets, and visualizations that align with specific business requirements.
Seamless embedded analytics: Integrate interactive dashboards directly into applications, portals, and workflows to deliver insights where users need them most.
AI-powered insights: Ask questions in plain English, generate dashboards faster, and turn complex data into clear summaries with Bold BI’s AI capabilities.
White-labeling capabilities: Customize dashboards, colors, fonts, logos, and branding to deliver analytics that look and feel like your own product.

In addition to earning recognition in the G2 Summer 2026 reports, Bold BI continues to help organizations overcome real-world analytics challenges and achieve meaningful business outcomes.

Customer Success Stories

Customer success stories demonstrate how organizations use Bold BI to simplify analytics, improve operational efficiency, and make better business decisions.

Backwind LLC: Making Business Data More Accessible Through Interactive Analytics

Backwind LLC needed a way to transform business data into meaningful insights that stakeholders could easily access and understand. Using Bold BI, the company implemented interactive dashboards that simplified data analysis, improved visibility into key metrics, and enabled more informed, data-driven decision-making.

Challenge: Limited visibility into business performance and difficulty accessing actionable insights.
How: Implemented interactive dashboards and visual analytics to transform complex business data into meaningful insights.
Why it matters: Enabled stakeholders to make faster, more confident business decisions.

Precision Systems: Reducing Manual Reporting with Embedded Dashboards

Precision Systems wanted to reduce the effort required for manual reporting while giving teams faster access to operational insights. By embedding Bold BI dashboards into existing workflows, the organization centralized reporting and made real-time analytics readily available across teams.

Outcome: Reduced manual reporting and improved access to operational analytics.
How: Embedded interactive dashboards and centralized reporting to provide real-time visibility into critical business data.
Why it matters: Increased operational efficiency by allowing teams to spend less time preparing reports and more time acting on insights.

Explore more real-world customer success stories on the Bold BI Case Studies page.

What Customers Say About Bold BI

Powerful and Flexible Analytics Platform with Intuitive Design
"Bold BI provides a powerful and flexible analytics platform that is easy to implement and integrate with various data sources. The dashboard designer is intuitive, allowing both technical and non-technical users to build meaningful visualizations quickly. Its ability to connect directly to databases, APIs, and web services makes it highly suitable for enterprise-level reporting. I also appreciate the strong customization options, embedding capabilities, overall performance with large datasets, and responsive customer support." By Mohamed Zulfadli A., G2

Simple, Powerful and Easy to Use
"I've been using Bold BI regularly, and it's been easy to set up and use. It has a clean, intuitive interface while still allowing you to build powerful dashboards and reports. I also like how easily it connects to a wide range of data sources and supports embedded analytics. Whether you're building for a small team or a large organization, it provides a practical, reliable, and flexible analytics experience." By Muhdin M., G2

Read more verified customer feedback on the Bold BI G2 Reviews page.

Turning Recognition into Better Analytics Experiences with Bold BI

We sincerely thank our customers and community for their continued support and valuable feedback. These 23 badges reflect real experiences from users who rely on Bold BI® to power their analytics workflows.

This recognition reinforce our commitment to delivering an intuitive, scalable, and high-performing analytics platform. Each milestone motivates us to continue improving usability, performance, and embedded analytics capabilities.

As organizations increasingly rely on data to drive growth, Bold BI remains focused on helping teams create powerful analytics experiences that deliver measurable value.

Ready to see Bold BI in action?

Start your free trial
Schedule a demo
Explore Bold BI features today!

Power BI Migration: How to Reuse Existing Queries in Bold BI

Bold BI by Syncfusion — Tue, 30 Jun 2026 13:16:33 +0000

TL;DR: If your Power BI dataset folds to SQL and View Native Query is available, you may be able to reuse or adapt that SQL in Bold BI® Code View subject to compatibility checks and validation. You still need to validate joins, filters, and data types, and you’ll typically rebuild visuals. Queries based on non-folding Power Query (M) steps, DAX measures, or import-only transformations usually can’t be reused as SQL.

Introduction

Power BI migrations often start with one practical question: Can we reuse what we already built? In some cases, native SQL generated through Power BI's query-folding process may be reusable in another analytics platform. Whether reuse is possible depends on factors such as connector support, database compatibility, permissions, and the transformations applied within Power Query. The extracted SQL can be copied, run against the same database, and validated in Bold BI before you rebuild dashboards.

If your model relies heavily on Power Query (M) steps that don’t fold, DAX measures, or import-only transformations, you’ll likely need to recreate that logic using SQL, views, or modeling within Bold BI. In this guide, we’ll walk through how to reuse Power BI queries in Bold BI, what to validate, and what to expect during the migration process.

What is Power BI Migration?

Power BI migration is the process of moving dashboards, datasets, and analytics workflows from Microsoft Power BI to another business intelligence platform while preserving data accuracy, business logic, and user continuity.

Power BI migration can involve different approaches, including:

Platform transition: Moving selected dashboards and analytics assets to another BI platform based on organizational requirements.
Analytics modernization: Updating analytics experiences while retaining validated business logic where possible.
Embedded analytics adoption: Integrating analytics into internal applications, customer-facing portals, or operational workflows.

The scope of migration depends on how Power BI is used within the organization, the complexity of existing assets, and the migration goals. Some components may be reusable, while others may require review, reconfiguration, or rebuilding in the target platform.

Power BI Migration Assessment: What to Review Before You Begin

Before beginning a Power BI migration, it is important to understand the scope of the existing analytics environment. A structured assessment may help teams document current assets, identify dependencies, and prioritize migration activities based on business requirements.

Consider reviewing the following areas:

Business-critical dashboards: Identify dashboards that support key business processes, operational workflows, or executive decision-making.
Data source dependencies: Document connected databases, cloud services, credentials, and connection requirements.
Query and transformation logic: Review SQL queries, Power Query transformations, DAX calculations, and other analytics logic used by dashboards.
Security and access controls: Map user roles, permissions, sharing settings, and governance requirements.
Embedded analytics touchpoints: Identify applications, portals, or workflows that depend on embedded dashboards or analytics components.
Usage and performance patterns: Review dashboard usage, refresh frequency, load times, and areas that may require optimization.

Completing an assessment before migration may help teams define scope, prioritize work, and plan the migration with fewer unexpected dependencies.

What Can Be Reused During Power BI Migration?

Not every Power BI asset can be transferred in the same way. While some components can be reused during migration, others typically need to be rebuilt or reconfigured. Understanding these distinctions early may help organizations plan migration projects more effectively and reduce the likelihood of unexpected redevelopment efforts.

Power BI Asset	Potential Reusability	Validation Required
Native SQL query logic (when available)	Varies by implementation	Yes
Data sources	May require reconfiguration	Yes
Dashboards/visuals	May require rebuilding or redesign	Yes
Security and permissions	Require review and reconfiguration	Yes

When a native SQL query is available, it may provide an opportunity for reuse because the logic already exists at the database layer. However, successful reuse depends on factors such as SQL compatibility, connector behavior, database permissions, parameter handling, and the target platform’s configuration.

Why Reusing Existing Queries Matters

A common concern during Power BI migration is the need to rebuild years of analytics logic from scratch. Recreating this logic manually can increase migration effort, introduce risk, and extend project timelines. Reusing existing queries can help mitigate these challenges while supporting business continuity.

Where query reuse is possible, organizations may benefit from:

Using existing query logic as a starting point for migration.
Reducing the amount of logic that needs to be recreated manually.
Supporting validation between the source and target systems.
Identifying business rules already implemented in SQL.
Providing a reference point when rebuilding analytics assets.

Actual benefits depend on the amount of reusable logic available and the level of validation, reconfiguration, or redesign required. Beyond technical savings, query reuse may help teams shift focus toward higher-value activities, such as:

Dashboard modernization.
User adoption and training.
Governance improvements.
Embedded analytics initiatives.
Broader analytics modernization projects.

By building on validated query logic, organizations can reduce migration complexity, lower the risk of errors during redevelopment, and maintain more

How to Migrate Power BI Queries into Bold BI

Reusing Power BI native SQL queries in Bold BI involves extracting the SQL query from Power BI, connecting Bold BI to the same data source, and validating the output before building dashboards. The following workflow is an example and may vary depending on the connector, data source, SQL compatibility, and dashboard design.

Step 1: Connect Power BI to the Data Source

Start in Power BI by connecting to your existing data source, such as Microsoft SQL Server. Select the required tables and load the data needed to create the query.

Step 2: Load the Required Tables into Power BI

Choose the tables you need and load them into Power BI. This prepares the data for transformation, joining, and query design.

Step 3: Open Power Query Editor

In Power BI, select Transform Data to open Power Query Editor. This is where you can shape the imported data, apply transformations, and prepare the query logic.

Step 4: Merge Queries If Needed

If your dashboard combines data from multiple tables, use Merge Queries as New to create a combined dataset. Choose the appropriate tables and matching fields.

Step 5: Select Join Type and Matching Columns

Choose the appropriate join type, such as Left Outer, and select the matching columns from both tables. This defines how the tables are merged and helps preserve the relationship used in your Power BI dataset.

Step 6: Review Applied Steps

Check the applied steps in Power Query Editor to confirm that the transformations and merge operations were applied correctly. This step is important because the native query generated by Power BI depends on the transformations applied.

Step 7: View the Native Query

In Power Query Editor, check whether View Native Query is available for the selected step. When query folding applies and the connector supports native query generation, this option may display the SQL sent to the underlying database. In some cases, the option may be unavailable or disabled because query folding has been interrupted or the connector does not support native query viewing.

Step 8: Copy the Native SQL Query

If a native SQL query is available, review and copy it for evaluation in the target environment. Before reuse, verify database references, parameters, filters, joins, and any database-specific syntax that may require modification.

select [$Outer].[CustomerID] as [CustomerID],

    [$Outer].[CompanyName] as [CompanyName],

    [$Outer].[ContactName] as [ContactName],

    [$Outer].[ContactTitle] as [ContactTitle],

    [$Outer].[Address] as [Address],

    [$Outer].[City] as [City],

    [$Outer].[Region] as [Region],

    [$Outer].[PostalCode] as [PostalCode],

    [$Outer].[Country] as [Country],

    [$Outer].[Phone] as [Phone],

    [$Outer].[Fax] as [Fax],

    [$Inner].[OrderID] as [Orders.OrderID],

    [$Inner].[CustomerID2] as [Orders.CustomerID],

    [$Inner].[EmployeeID] as [Orders.EmployeeID],

    [$Inner].[OrderDate] as [Orders.OrderDate],

    [$Inner].[RequiredDate] as [Orders.RequiredDate],

    [$Inner].[ShippedDate] as [Orders.ShippedDate],

    [$Inner].[ShipVia] as [Orders.ShipVia],

    [$Inner].[Freight] as [Orders.Freight],

    [$Inner].[ShipName] as [Orders.ShipName],

    [$Inner].[ShipAddress] as [Orders.ShipAddress],

    [$Inner].[ShipCity] as [Orders.ShipCity],

    [$Inner].[ShipRegion] as [Orders.ShipRegion],

    [$Inner].[ShipPostalCode] as [Orders.ShipPostalCode],

    [$Inner].[ShipCountry_1] as [Orders.ShipCountry_1]

from [dbo].[Customers] as [$Outer]

left outer join

(

    select [OrderID] as [OrderID],

        [CustomerID] as [CustomerID2],

        [EmployeeID] as [EmployeeID],

        [OrderDate] as [OrderDate],

        [RequiredDate] as [RequiredDate],

        [ShippedDate] as [ShippedDate],

        [ShipVia] as [ShipVia],

        [Freight] as [Freight],

        [ShipName] as [ShipName],

        [ShipAddress] as [ShipAddress],

        [ShipCity] as [ShipCity],

        [ShipRegion] as [ShipRegion],

        [ShipPostalCode] as [ShipPostalCode],

        [ShipCountry_1] as [ShipCountry_1]

    from [dbo].[Orders] as [$Table]

) as [$Inner] on ([$Outer].[CustomerID] = [$Inner].[CustomerID2])

Step 9: Connect Bold BI to the Same Data Source

Log in to Bold BI and create a new data source. Connect Bold BI to the same database used in Power BI, such as Microsoft SQL Server.

Step 10: Enable Code View in Bold BI

In the Bold BI data design interface, switch to Code View. This opens the query editor where you can enter custom SQL.

Step 11: Paste the Power BI Native Query

Paste the SQL query into the Bold BI query editor and test it against the connected data source. Depending on the database platform, connector, driver, permissions, and SQL syntax used, adjustments may be required before the query executes successfully.

Step 12: Execute and Preview the Query

Run the query in Bold BI and preview the results. Compare the output with the original Power BI dataset to confirm that the data matches.

Step 13: Save the Data Source

Once the query executes successfully, save it as a Bold BI data source with a meaningful name. Use a naming convention that makes it easy for other team members to identify the migrated query.

Step 14: Build and Preview the Dashboard

Use the saved data source to build a dashboard in Bold BI. Add visualizations, configure widgets, and preview the dashboard to confirm that the migrated data supports the required analytics experience.

Why this matters: Even when the query output matches, dashboard layouts, filters, and interactions still need to be reviewed in Bold BI to ensure the final analytics experience meets user requirements.

For a detailed walkthrough, refer to our guide on migrating Power BI queries into Bold BI.

Best Practices for Query Migration

To keep migrated queries accurate and reliable, validation should begin early in the migration process, not after dashboards have already been rebuilt.

Follow these best practices:

Start small: Begin with high-value dashboards that use simpler native SQL queries. This helps teams test the migration approach before moving to more complex assets.
Validate early: Compare row counts, column mappings, filters, joins, data types, calculated fields, and KPI output before rebuilding dashboards.
Review dependencies: Check parameters, authentication settings, data source connections, calculated fields, and any Power BI-specific logic that may affect query behavior.
Maintain documentation: Track copied queries, changes made during migration, validation results, and naming conventions so teams can review and troubleshoot later.
Involve business users: Ask business stakeholders to review migrated output and dashboard results to confirm that the data still reflects expected business logic.

A phased approach helps teams validate results incrementally, reduce migration risk, and refine queries before expanding migration work.

Ready to Reuse Your Existing Power BI Queries?

Power BI migration does not necessarily require recreating every component from the beginning. In environments where native SQL queries are available and compatible with the target platform, portions of existing query logic may be reusable after review and validation. The amount of reuse achievable depends on the underlying implementation, data sources, transformations, and analytics requirements.

To evaluate the best migration approach for your environment, start a 30-day free trial or request a personalized demo to see how Bold BI aligns with your existing Power BI implementation.

Frequently Asked Questions

1.
Can Power BI queries be reused in Bold BI?
In some cases. When a native SQL query is available and compatible with the target environment, organizations may be able to reuse or adapt portions of that query logic in Bold BI. Compatibility depends on factors such as query-folding behavior, connector support, database configuration, permissions, and SQL syntax.
2.
Do Power BI dashboards migrate automatically to Bold BI?
No. Dashboards and visualizations may require recreation or reconfiguration in Bold BI, depending on their design and complexity.
3.
What is the biggest benefit of reusing Power BI queries?
One benefit is reduced redevelopment effort, as organizations can reuse portions of existing query logic instead of recreating it from scratch.
4.
What should I validate after migrating a query?
Validate row counts, data types, joins, filters, calculated values, KPI output, and dashboard-level results to confirm accuracy.
5.
Can all Power BI queries be migrated to Bold BI?

No. Some Power BI implementations contain logic that does not translate directly into reusable SQL. Examples may include non-folding Power Query transformations, DAX measures, calculated tables, semantic model features, and connector-specific functionality. Migration requirements should be evaluated based on the specific implementation.
6.
Does query reuse reduce Power BI migration costs?
Query reuse can reduce effort in some cases, but total cost depends on scope, complexity, and required rebuild work.
7.
When should organizations consider Bold BI during a Power BI migration?
Organizations may evaluate Bold BI during a migration when they are assessing platform options for dashboard development, embedded analytics initiatives, deployment preferences, data connectivity requirements, and reporting workflows. The right fit depends on the organization's technical, operational, and business requirements.

AI-Native vs. AI-Augmented Analytics: Differences in 2026

Bold BI by Syncfusion — Mon, 29 Jun 2026 06:48:23 +0000

TL;DR: AI-augmented analytics enhances existing BI with AI-driven insights and automation. AI-native analytics uses AI as the primary interface through conversations and agents. Most organizations start with AI-augmented analytics to preserve existing BI and governance. AI-native analytics suits AI-first products and advanced, conversational experiences. The right choice depends on your goals, analytics maturity, and governance needs.

Introduction

Artificial intelligence is rapidly changing how organizations interact with data. Instead of relying solely on dashboards, businesses increasingly expect conversational experiences, automated insights, and AI-assisted decision-making. This shift has introduced two emerging approaches: AI-augmented analytics and AI-native analytics. Although the terms are often used interchangeably, they represent very different ways of bringing AI into the analytics experience.

AI-augmented analytics enhances existing business intelligence workflows with AI-powered assistance, while AI-native analytics makes AI the primary interface for exploring and understanding data. Understanding the difference is essential for organizations evaluating analytics platforms, planning AI initiatives, or defining their long-term data strategy.

In this guide, we'll explain how AI-augmented analytics and AI-native analytics differ, where each approach works best, and how to determine which model fits your business needs.

What Is AI-Augmented Analytics?

AI-augmented analytics improves existing BI workflows by adding AI support to the analytics process. It helps users ask questions in natural language, discover insights faster, summarize dashboards, and identify trends without manually searching through multiple dashboards.

For example:

If a retail company sees a 12% drop in regional revenue after a pricing change, users can ask, “Why did revenue decline in the Northeast region?” The system can analyze pricing, inventory, customer behavior, and sales performance to highlight possible causes.

Rather than replacing analysts, AI-augmented analytics helps them spend less time preparing data and more time validating insights and making decisions.

Key Capabilities of AI-Augmented Analytics

Modern AI-augmented analytics platforms typically include capabilities such as:

Natural language querying: Allows users to ask business questions conversationally, making it easier for non-technical users to explore data without needing SQL or advanced analytical skills.
AI-generated visualizations: Enables users to create charts, graphs, and dashboards from simple text prompts, reducing the time and effort required to build visual analytics.
Dashboard summaries: Automatically generate clear, plain-language explanations of key insights, helping users quickly understand complex data without manual interpretation.
Automated trend and anomaly detection: Continuously analyzes data to identify patterns, trends, and unusual changes, allowing organizations to respond to issues or opportunities in real time.
Predictive insights: Uses historical data to forecast future outcomes, helping businesses anticipate trends and make proactive, data-driven decisions.
Self-service analytics: Empowers business users to access and analyze data independently, reducing reliance on technical teams and accelerating decision-making across the organization.

These capabilities make analytics more accessible to business users while improving productivity for analysts and decision-makers.

When Should Organizations Use AI-Augmented Analytics?

AI-augmented analytics is well suited for organizations that want to modernize analytics without replacing their existing BI ecosystem.

Common scenarios include:

Enhancing executive dashboards with AI-assisted insights.
Expanding self-service analytics for business users.
Improving operational analytics and KPI monitoring.
Delivering AI-enhanced embedded analytics within SaaS applications.
Helping teams identify trends and performance changes more quickly.

Because it enhances familiar BI workflows instead of replacing them, AI-augmented analytics often provides a lower-risk path to AI adoption.

Pros and Cons of AI-Augmented Analytics

AI-augmented analytics helps organizations modernize business intelligence without replacing their existing analytics ecosystem.

Pros

Organizations benefit from AI-augmented analytics because it can:

Improve productivity by reducing repetitive analytical tasks.
Expand access to analytics for non-technical users.
Support existing BI investments and governance frameworks.
Accelerate insight discovery and decision-making.

Cons

Despite its advantages, organizations should also consider that:

AI insights are only as reliable as the underlying data quality.
Users still need business context to interpret recommendations effectively.
Some organizations may need to modernize legacy data environments to realize the full value of AI capabilities.

Best for

AI-augmented analytics is ideal for organizations that want to enhance analytics, dashboards, and business intelligence while maintaining governance and human oversight.

To learn more about AI-augmented analytics, check our blog on "Augmented Analytics: Gain Business Insight for Faster Decision-Making."

What Is AI-Native Analytics?

AI-native analytics is designed with AI as the primary interaction model. Instead of using dashboards as the main starting point, users interact with data through conversational interfaces, AI assistants, or AI agents.

For example, a user might ask:

"Which customer segments are most likely to churn this quarter?" An AI-native analytics platform can analyze multiple datasets, identify risk factors, recommend actions, and generate supporting visuals within a conversational experience.

The goal is to make analytics feel less like operating an analytics tool and more like working with an intelligent data assistant.

Key Capabilities of AI-Native Analytics

AI-native analytics platforms often include:

Conversational analytics (LLM-powered): Users ask questions in plain language and receive answers with supporting visuals and definitions.
AI assistants that proactively monitor business metrics: Use intelligent agents to continuously track performance and alert users about important changes, risks, or opportunities without requiring manual checks.
Automated insight generation: Automatically analyzes datasets to uncover patterns, correlations, and key insights, reducing the need for manual data exploration.
AI-created dashboards and analytics: Generates dashboards and analytics dynamically based on user queries or business context, eliminating the need to build them manually.
Context-aware recommendations: Provides tailored suggestions based on business context, user behavior, and data patterns, helping users make more informed decisions.

These capabilities reduce the effort required to find insights while making analytics more accessible to users with little technical expertise.

When Should Organizations Use AI-Native Analytics?

AI-native analytics is best suited for organizations building AI-first experiences or looking to fundamentally change how users interact with data.

Typical use cases include:

AI-powered customer analytics portals.
Conversational business intelligence.
AI assistants for executives.
Intelligent SaaS applications.
Personalized analytics experiences.
Agent-based monitoring and decision support.

Organizations pursuing digital transformation initiatives or AI-first product strategies often evaluate AI-native analytics as part of their long-term vision.

Pros and Cons of AI-Native Analytics

AI-native analytics reimagines analytics as an AI-first experience rather than an enhancement to traditional BI.

Pros

Organizations adopting AI-native analytics can:

Deliver conversational analytics experiences.
Reduce the learning curve for nontechnical users.
Automate insight discovery across multiple datasets.
Provide proactive recommendations instead of reactive analytics.
Build AI-first products and customer-facing analytics experiences.

Cons

AI-native analytics also introduces new challenges:

Implementation often requires significant changes to existing analytics workflows.
Strong AI governance and explainability have become essential.
Organizations must carefully validate AI-generated recommendations.
Users may need time to build confidence in AI-led decision support.
Integration with legacy BI environments can be more complex.

Best for

AI-native analytics is best suited for organizations building AI-first products, conversational analytics experiences, or next-generation SaaS applications.

AI-Native vs. AI-Augmented Analytics: Key Differences

Understanding the differences between AI-native analytics and AI-augmented analytics helps organizations choose the right approach based on their technology goals, user needs, and long-term data strategy.

Aspect	AI-Augmented Analytics	AI-Native Analytics
Primary goal	Enhance existing BI workflows with AI assistance	Make AI the primary way users interact with data
Starting point	Dashboards and KPIs	Questions, conversations, and prompts
Role of AI	Assistant within existing workflows	Primary interaction model
User experience	Dashboard-first with AI support	Conversation-first with AI assistance
Insight discovery	AI-assisted with human validation	AI-driven with contextual recommendations
Natural language capabilities	Supported as a feature	Often the primary interface
Implementation approach	Extends existing BI investments	May require new workflows and architectures
Governance model	Builds on existing BI governance	Requires additional AI governance and validation

The biggest distinction is not the technology itself, but how AI participates in the analytics experience. AI-augmented analytics improves the way people already work with dashboards and analytics. AI-native analytics redesigns the analytics experience around AI as the primary interaction model.

Which Approach Is Right for Your Organization in 2026?

There is no single “best” choice between AI-augmented analytics and AI-native analytics. The right approach depends on your business goals, analytics maturity, and how your teams use data.

Choose AI-augmented analytics if you want to:

Modernize existing BI and analytics investments.
Improve self-service analytics adoption.
Introduce AI capabilities without disrupting current workflows.
Maintain established governance and analytics processes.

Choose AI-native analytics if you want to:

Build AI-first products and experiences.
Deliver conversational analytics to users or customers.
Enable AI assistants and intelligent applications.
Redesign analytics workflows around AI interactions.

In summary, AI-augmented analytics improves existing workflows, while AI-native analytics introduces a more AI-first approach to data interaction.

Most organizations start with AI-augmented analytics and gradually explore AI-native capabilities as their needs evolve.

Once organizations understand the differences between AI-augmented and AI-native analytics, the next step is evaluating how modern analytics platforms support AI adoption. Bold BI® follows an AI-augmented approach that enhances existing BI investments while introducing AI-powered capabilities for faster insight discovery.

How Bold BI Supports AI-Augmented Analytics

Many organizations want to adopt AI-powered analytics without disrupting existing dashboards, governance policies, or reporting workflows. AI-augmented analytics provides a practical path by enhancing existing business intelligence experiences with AI-powered assistance rather than replacing them entirely.

Bold BI follows this approach by combining interactive dashboards, self-service analytics, embedded analytics, and AI-powered capabilities that help users explore data using natural language while maintaining enterprise security and governance.

With Bold BI, organizations can enhance their analytics strategy through:

Unified AI Agent: Helps users explore connected data sources through text-based queries, generate visualizations and dashboard elements, and summarize dashboards.

AI Copilot: Helps users create and customize visualizations from plain English prompts, reducing the need for manual dashboard design.
Smart Narrations: Automatically generates plain-language explanations for dashboard widgets, making insights easier to understand and share.
Prompt-to-Dashboard: Converts natural language prompts into interactive dashboards, reducing dashboard development effort.

Embedded AI Analytics: Brings AI-powered insights into business applications and customer-facing portals using embedding APIs and SDKs.

This phased approach allows organizations to adopt AI-augmented analytics without replacing existing BI investments or disrupting established workflows. Rather than replacing dashboards or human expertise, Bold BI uses AI to simplify data exploration, accelerate insight discovery, and help organizations make faster, more informed decisions while maintaining enterprise governance and security.

If your organization is looking to modernize analytics with AI-powered dashboards, natural language insights, and embedded analytics, explore how Bold BI helps teams adopt AI while preserving existing BI investments and enterprise governance. Start your free trial or request a personalized demo today.

Related Resources:

The following frequently asked questions address common considerations when evaluating AI-augmented and AI-native analytics.

Frequently Asked Questions

Will AI-native analytics replace traditional dashboards?
No. Dashboards remain essential for KPI monitoring and governed analytics. Many organizations are expected to use dashboards and conversational analytics together.
Can organizations use AI-augmented analytics and AI-native analytics together?
Yes. Many organizations use AI-augmented analytics for internal BI and analytics while adopting AI-native experiences for conversational analytics and customer-facing applications.
How should organizations start adopting AI in analytics?
Many organizations begin with AI-augmented analytics because it builds on existing BI investments and governance models while allowing AI adoption to happen gradually.
Is AI-augmented analytics suitable for embedded analytics?
Yes. AI-augmented analytics can enhance embedded analytics by enabling natural language interactions, AI-assisted insights, and self-service data exploration directly within business applications.

Embed Analytics in a Blazor Application Using JavaScript

Bold BI by Syncfusion — Mon, 29 Jun 2026 06:42:14 +0000

TL;DR: Bold BI® lets you embed interactive dashboards in Blazor Server apps using the Bold BI JavaScript SDK and token-based embed authentication, so users can explore analytics without leaving your application.

Introduction

Users expect analytics to be available inside the tools they use every day, but building dashboard functionality from scratch is time-consuming and complex. Bold BI supports dashboard embedding in Blazor applications, enabling developers to quickly integrate interactive analytics via JavaScript embedding. In this guide, you will learn how to embed a Bold BI dashboard in a Blazor application using JavaScript Embedding.

Let’s get started.

Why Embed Dashboards in Blazor Applications?

Embedded analytics brings insights directly into the application experience and closes workflow gaps. When dashboards exist in separate tools, users switch between systems, which slows workflows and reduces efficiency. Key advantages include:

Unified user experience: Keeping dashboards within the application provides a seamless interface where users can access data without leaving their workflow.
Faster decision-making: Real-time access to analytics enables users to take immediate action based on up-to-date insights.
Centralized security control: Authentication and access can be managed within the application, ensuring consistent and secure data governance.
Higher user engagement: Embedding analytics into daily workflows encourages users to interact more frequently with dashboards and insights.
Scalability and flexibility: Embedded analytics makes it easier to extend insights across users, teams, and applications without rebuilding systems.

Next, let's walk through the process of integrating a Bold BI dashboard into a Blazor Server application.

How to Embed a Dashboard in a Blazor Application

The following steps demonstrate how to configure embedding, set up the required components, and render dashboards in a Blazor Server application using JavaScript embedding.

Prerequisites

Before you begin, ensure the following requirements are met:

Step 1: Set Up a Blazor Server App

Start by creating a new Blazor Server project.

Create a folder in your desired location and open it in Visual Studio Code. Open the terminal and run:

dotnet new blazorserver

This will serve as the application where your dashboard will be embedded.

Step 2: Enable Embedding in Bold BI

Next, prepare your dashboard for embedding:

Select the dashboard you want to embed

[caption id="" align="alignnone" width="1913"]

Go to Settings → Embed

[caption id="" align="alignnone" width="1903"]

[caption id="" align="alignnone" width="1619"]

Enable Embed Authentication

[caption id="" align="alignnone" width="1899"]

Click Embed Configuration.
Download embedConfig.json file.

[caption id="" align="alignnone" width="1127"]
This file contains all the essential details needed to connect your app to Bold BI.

Step 3: Add the Configuration to Your Project

Place the downloaded embedConfig.json file in your Blazor project.

Your application will read this file to retrieve values such as:

Server URL
Dashboard ID
User email
Embed secret

These are required to securely render the dashboard.

Step 4: Configure the Application

At this stage, your application needs to load the embedding configuration and make it available to the frontend.

To achieve this, you will:

Read the embedConfig.json file
Deserialize the configuration values
Pass them to the frontend using ViewBag

This prepares all required values for frontend use.

Step 5: Add the Dashboard Container

Open your Blazor page for example, Dashboard.razor and add a container along with the required Bold BI scripts and configuration variables to host and initialize the embedded dashboard:

<head>
<title>BoldBI Blazor Embed</title>
<script type="text/javascript" src="https://cdn.boldbi.com/embedded-sdk/latest/boldbi-embed.js"></script>
<script type="text/javascript" src="~/js/Index.js"></script>
<link rel="stylesheet" href="~/css/site.css" />
<script type="text/javascript">
var rootUrl = "@ViewBag.ServerUrl";
var dashboardId = "@ViewBag.DashboardId";
var siteIdentifier = "@ViewBag.SiteIdentifier";
var environment = "@ViewBag.Environment";
var embedType = "@ViewBag.EmbedType";
var authorizationServerUrl = '@Url.Action("AuthorizationServer", "EmbedData")';
</script>
</head>

<body onload="renderDashboard(dashboardId)">
<div id="viewer-section" style="width: 100%;">
<div id="dashboard"></div>
</div>
</body>

Step 6: Add JavaScript Embedding Logic

Use the Bold BI JavaScript SDK to render the dashboard.

In your JavaScript file (e.g., wwwroot/js/BoldBI.js), configure:

Server URL
Site identifier
Dashboard ID
Embed token

window.boldbiEmbed = {
  renderDashboard: function () {
    const dashboard = BoldBI.create({
      serverUrl: window.rootUrl + "/" + window.siteIdentifier,
      dashboardId: window.dashboardId,
      embedContainerId: "dashboard",
      width: "100%",
      height: "100%",
      authorizationServer: { url: window.authorizationServerUrl }
    });

    dashboard.loadDashboard();
  }
};

Ensure these values match those in your configuration file.

Step 7: Run the Application

Now run your project:

dotnet restore
dotnet watch run

Step 8: Verify the Dashboard

If everything is configured correctly, you should see:

The dashboard will load inside your app.
Charts and widgets will be visible.
No authentication errors will appear.

At this point, your embedded analytics setup is complete.

[caption id="" align="alignnone" width="1674"]
To learn more about implementation details and explore sample projects, refer to the official Bold BI guide for ''How to Embed a Bold BI Dashboard in a Blazor Server Application''.

Now that you’ve completed the embedding steps, let’s see how this implementation applies in a real-world scenario.

How Dashboard Embedding Works in a Blazor Server Application

The Blazor Server sample follows a sequence of configuration loading, secure token generation, token handling, and dashboard rendering.

How It Works

Blazor application requests a secure embed token.
Authorization API generates the request and signature.
Bold BI server validates the embed secret and request.
Bold BI server returns the embed token.
Authorization API returns the token to the Blazor application.
Blazor application uses the token with the Bold BI JavaScript SDK to render the dashboard.

[caption id="" align="alignnone" width="1536"]]

To learn more, refer to the official Bold BI documentation on how the Blazor Server sample works.

Why Choose Bold BI for Dashboard Embedding?

Bold BI® is well suited for embedding analytics in Blazor applications, enabling secure and flexible integration into existing workflows. With token-based authentication, interactive dashboards, and scalable embedding options, it helps developers deliver seamless analytics experiences. With Bold BI, organizations can:

Embed dashboards directly within application workflows
Maintain secure access through token-based authentication
Deliver interactive analytics experiences to users
Support role-based access control and governance
Accelerate analytics delivery without building analytics infrastructure from scratch
Scale analytics across users, teams, and applications
Support multi-tenant analytics for SaaS applications

Whether you are building SaaS platforms, internal applications, or customer-facing portals, Bold BI provides a reliable way to integrate analytics into your application.

Ready to get started? Try embedding a dashboard in your Blazor Server application and see how Bold BI can bring interactive data insights directly into your workflows. For deeper guidance, start a free trial, explore the documentation, or request a demo to see how it fits your use case.

Frequently Asked Questions

1.
Can I embed multiple dashboards in a Blazor Server application?
Yes. You can embed multiple Bold BI dashboards in the same Blazor Server application by configuring separate dashboard instances.
2.
How can I secure embedded dashboards for different users?
Use authentication and role-based access control to ensure users only access the dashboards and data they are authorized to view.
3.
Is authentication required for embedding?
Yes. Authentication is recommended to protect embedded dashboards from unauthorized access.
4.
Can I customize dashboard appearance?
Yes. You can customize themes, colors, layouts, and other UI elements to match your application’s branding.
5.
Does embedding dashboards affect application performance?
Performance depends on dashboard complexity, data volume, and network conditions. With proper configuration, embedded dashboards can run efficiently in Blazor applications.
6.
What are the prerequisites for embedding a Bold BI dashboard in Blazor?
You need a Bold BI environment, a published dashboard, embedding enabled, required configuration values, and a Blazor application set up for integration.
7.
How do I embed analytics in a Blazor Server application?
Enable dashboard embedding, configure authentication, add the required settings, and render the dashboard using JavaScript interop.

How to Secure Multiple CSV Files in Dashboards with RLS

Bold BI by Syncfusion — Fri, 26 Jun 2026 08:11:54 +0000

TL;DR: Learn how to apply row-level security (RLS) across multiple CSV files in Bold BI® by combining datasets and using user-based filters. This approach simplifies dashboard access control, reduces duplication, and ensures each user sees only relevant data.

Introduction

Managing dashboard access control becomes challenging when data is distributed across multiple CSV files. As usage grows, controlling what each user can see becomes essential for maintaining data security. CSV files are widely used to share analytics data, with teams exporting sales records, customer data, inventory updates, and operational metrics before building dashboards. While this approach is convenient, it introduces a key challenge: how can you prevent users from viewing restricted data without creating duplicate dashboards?

For example:

A regional sales manager should see only region-specific data.
A department head should access only relevant records.
Executives may require full visibility.

Without proper control, sensitive data can be exposed.

This is where row-level security (RLS) across appended CSV datasets becomes essential. By combining datasets and applying one RLS rule, you can control access using user-based filters and maintain consistent dashboard access control.

In this article, you'll learn how to secure multiple CSV files in dashboards using a single RLS rule in Bold BI.

What Is Row-Level Security in Dashboards?

Row-level security (RLS) is a data access control mechanism that restricts which rows of data users can view within a dashboard. Unlike dashboard-level permissions, which determine who can open a dashboard, RLS controls what data users can see after gaining access.

For example:

User	Region	Visible Data
John	East	East Region Sales
Sarah	West	West Region Sales
Admin	All	All Regions

All users access the same dashboard, but each sees only the records they are authorized to view.

It works by applying filters tied to user identities within the dataset itself, ensuring consistent data source security across dashboards. For embedded analytics scenarios, it can also be applied programmatically using the embedding SDK.

Why Row-Level Security Matters for Multi-CSV Dashboards

When working with multiple CSV files, data is often spread across different datasets, such as sales, inventory, products, or operations. While combining these files into a dashboard provides a unified view, managing secure access becomes increasingly challenging as the number of users grows.

Without row-level security (RLS):

Users may gain access to records they are not authorized to view.
Sensitive business data can be exposed to unintended audiences.
Organizations may need to create separate dashboards for different user groups.
Maintaining multiple dashboard versions increases administrative overhead and the risk of inconsistencies.

A more scalable approach is to combine multiple CSV files into a single data source and apply a centralized row-level security rule. When users access the dashboard, Bold BI automatically filters the data based on the defined RLS conditions, ensuring each user sees only the information relevant to their role or permissions.

Key benefits include:

Simplified administration: Manage a single RLS rule instead of maintaining separate filters across multiple CSV datasets.
Improved scalability: Add new users or CSV files without redesigning your security strategy.
Consistent user experience: Allow everyone to access the same dashboard while viewing only the data they are authorized to see.
Reduced maintenance: Eliminate the need to create and manage duplicate dashboards for different user groups.
Enhanced data security: Protect sensitive information through centralized, user-based filtering.

If you're embedding dashboards into your applications, see Enabling Row-Level Security with Embedded BI to learn how RLS can be applied in embedded analytics scenarios while maintaining secure, user-specific data access.

Next, let's explore how to implement this setup in Bold BI.

How to Secure Multiple CSV Files with RLS in Bold BI

Applying row-level security to a unified dataset may seem complex, but Bold BI simplifies it with built-in data preparation and filtering features. By combining files and applying filters, you can control access efficiently across users.

Prerequisites

Multiple CSV files with a consistent structure for easier appending.
A column for filtering such as region, department, or user ID.
Access to Bold BI with appropriate user roles.

Follow the steps below to get started.

Step 1: Open the Data Sources page

Log in to your Bold BI dashboard. From the left navigation pane, click the Data Sources icon, as shown below. This opens the Data Sources page where you can create and manage all your datasets.

Step 2: Create a new CSV data source

On the Data Sources page, click New Data Source in the top-right corner. From the dropdown, select Create Data Source, as highlighted in the image. This will open the connector selection panel.

This will open the connector selection panel as shown below.

Step 3: Select the CSV connector and upload your file

In the Add Data Source dialog, you will see a list of available connectors. Use the search bar to find CSV, then select the CSV connector from the results.

Step 4: Upload your CSV file and configure the data source

Enter a name for your data source and optionally add a description. Then upload your CSV file by either dragging it into the upload area or clicking Browse File.

You can also configure options such as:

Selecting the file or folder input type.
Choosing the separator format.
Enabling the first row as header.

Once the file is uploaded successfully, click Connect to proceed.

Step 5: Preview and confirm your dataset

Review the data preview carefully to ensure the columns and values are loaded correctly. Once verified, click Connect to create the data source and proceed to the data preparation view.

Step 6: Append additional CSV files

In the Data Relation dialog, click the Append Data option in the top toolbar, as highlighted. This opens the Append Data From File dialog.

Upload additional CSV files using the upload area. Once the files show as Ready to append, click Append to merge them into the dataset.

Once appended, all files function as a single unified dataset, making it possible to apply a single RLS rule across all data sources. You can refer to our detailed guide on how to append CSV files for more advanced scenarios.

Step 7: Apply the RLS rule to control user data access

In the data preparation interface, click the Row-Level Security (User Filter) icon from the top toolbar, as highlighted in the image.

This opens the Setup Row Level Security (User Filter) dialog, as shown in the image.

In the Setup Row Level Security (User Filter) dialog, select a column that defines access control, such as region or department, and map it to specific users or user groups. For example, a user assigned to the east region sees only east region data and a user assigned to the west region sees only west region data.

For example:

A user assigned to East sees only East data.
A user assigned to West sees only West data.

After mapping the users to their data values, click Save.

To fully secure your dashboards, especially when working with multiple CSV data sources, you can combine row-level security with role-based access control.

Step 8: Use the secured data in your dashboard

Open the Dashboard Designer and use the dataset with the applied RLS configuration. Add charts, tables, or KPIs to visualize your data.

The RLS user filter is applied at query time, so each user’s dashboard view automatically reflects only the rows they’re allowed to access.

Step 9: Test what each user can see

Use the Preview as option in the dashboard to test different users.

For example:

North Manager sees only North region data.
South Manager sees only South region data.

As shown below, the dashboard preview updates based on the selected user.

Refer to this guide for detailed guidance on how to apply row-level security to multiple CSV files in Bold BI.

Practical Use Cases of Row-Level Security in Multi-CSV Dashboards

The following examples demonstrate how a single dashboard can securely serve different users by displaying only the data they are authorized to access.

Retail and Inventory Analytics

Retail teams often combine multiple CSV files, such as sales, inventory, and product data, into a single dashboard to get a unified view of operations. With Row-Level Security (RLS), this shared data is filtered based on the logged-in user.

View 1: Admin
The admin has unrestricted access to the complete dataset across all organizations. They can switch between organizations, such as Alpha Electronics, Beta Enterprise, Gamma Industries, and Delta Industries, while maintaining full visibility into every category, product, and business trend.

View 2: General manager
The General Manager at Alpha Electronics views the same dashboard layout, but the data is filtered based on their assigned scope, such as a specific region or store. While the dashboard visuals remain the same, only authorized data is displayed. Different General Managers can also have different levels of access based on their assigned permissions.

This approach lets multiple CSV datasets power a single dashboard while ensuring each user sees only relevant data.

Healthcare Operations

Healthcare teams bring together multiple CSV files, such as appointments, hospital records, and operational data, into a single dashboard for a consolidated view. With row-level security, access to this shared data is controlled based on the logged-in user's permissions.

View 1: Admin
The admin has unrestricted access across all healthcare networks, enabling a complete overview of hospitals, departments, patient activity, and operational metrics. They can switch between healthcare organizations while retaining full visibility into the underlying data.

View 2: General manager
A General Manager at Alpha Healthcare sees the same dashboard layout, but the data is filtered according to their assigned scope, such as specific hospitals or departments. While the visuals remain consistent, only authorized data is displayed. Even within the same healthcare network, different managers may see different data based on their assigned permissions.

This setup allows one dashboard to securely serve multiple users while reusing the same CSV data sources.

Next, let’s look at the common challenges and how you can ensure your setup remains efficient, secure, and easy to manage as your data and users grow.

Common Challenges and Best Practices for Securing CSV-Based Dashboards

While applying row-level security is straightforward, certain issues can impact accuracy and performance. Addressing these early and following best practices helps maintain a secure and scalable setup.

Common challenges

When applying row-level security to consolidated CSV data, the following issues can arise:

Schema mismatch: Differences in column names or data types can cause append failures.
Missing security column: Required fields such as region or department may be absent.
Incorrect user mappings: Mismatched values can expose incorrect data.
Empty dashboard results: Filters that do not match any records can result in blank visuals.
Duplicate records: Improper data preparation can introduce duplicate rows.

Best practices

To ensure a secure and maintainable setup, follow these best practices:

Standardize CSV structures before combining files.
Use consistent and meaningful fields such as Region or Department for access control.
Maintain user mappings with consistent formatting and values.
Validate datasets before applying security rules.
Test dashboard access regularly when updating users or data.

[elementor-template id="150021"]

Get Started with Secure Dashboard Access with Bold BI

Managing a secure dashboard becomes more challenging when multiple CSV files are involved, especially when different users need access to different subsets of data. Bold BI® makes it easy to combine CSV datasets, apply row-level security, and deliver personalized dashboards from a single, scalable platform. By combining multiple CSV datasets and applying a single Row-Level Security rule, organizations can deliver personalized dashboards, reduce maintenance, and secure sensitive information without creating duplicate dashboards.

With Bold BI, you can:

Combine multiple CSV files into a unified dashboard environment.
Apply row-level security (RLS) to control data visibility for specific users and roles.
Secure sensitive data without creating separate dashboards for different audiences.
Personalize dashboard views with dynamic, user-based filtering.
Support multi-tenant analytics for teams, departments, and customers.
Simplify data governance while maintaining flexibility and control.
Scale securely as your users, dashboards, and CSV data sources grow.

Whether you're building internal analytics solutions or customer-facing analytics portals, Bold BI helps you create secure CSV dashboards that automatically display the right data to the right users.

Ready to secure your CSV dashboards with row-level security? Start your free trial or request a personalized demo to see how row-level security can help you secure CSV dashboards at scale.

Frequently Asked Questions

What is row-level security?
Row-level security (RLS) is a data filtering mechanism that ensures users only see the rows of data they are authorized to access within a dashboard.
How do I secure dashboard access?
Securing a dashboard typically involves two layers. Role-based access control determines who can access the dashboard, while row-level security controls what data users can see after accessing it.
Can one RLS rule work across multiple datasets?
Yes. When multiple datasets (such as CSV files) are combined into a single data source, a single RLS rule can be applied to control data visibility across the entire dataset.
What happens when new users are added?
Simply update the user mappings associated with the RLS rule. There is no need to create new dashboards or duplicate datasets.

AI-Powered Dashboards: Why AI-Powered Dashboards: Why Enterprises Need Them in 2026

Bold BI by Syncfusion — Tue, 23 Jun 2026 11:26:21 +0000

TL;DR:

Enterprises don’t struggle to collect data, they struggle to turn it into decisions fast.

Traditional dashboards explain what happened; AI-powered dashboards help explain why, predict what’s next, and suggest where to look.

With natural-language exploration and automated insights, teams reduce analyst bottlenecks and scale self-service analytics.

Bold BI® combines AI-assisted analysis, conversational exploration, and embedded dashboards to bring insights into everyday workflows.

Introduction

Organizations have invested heavily in business intelligence tools, yet many teams still struggle to make timely, confident decisions. Product managers depend on analysts for answers, and executives spend valuable time reconciling conflicting metrics across multiple tools.

The challenge isn’t access to data; it’s the speed and clarity of decision-making. Traditional dashboards are designed to show what happened, but they rarely explain why it happened or what actions should follow. As decision windows continue to shrink in fast-moving markets, relying solely on historical visualization is no longer enough.

This is where AI-powered dashboards are reshaping modern analytics. By moving beyond static reports, they enable teams to uncover insights faster, understand underlying drivers, and act with confidence in real time.

What are AI‑powered dashboards?

AI-powered dashboards combine BI with AI techniques (such as anomaly detection, forecasting, and natural-language querying) to surface insights proactively, not just visualize metrics. Unlike traditional dashboards that simply display information, AI-powered dashboards actively assist users by:

Automatically surface notable changes in KPIs (spikes, drops, trend breaks).
Detect anomalies and outliers that warrant investigation.
Provide plain-language explanations for metric movement (drivers, segments, contributing factors).
Forecast likely outcomes based on historical patterns.
Answer questions via natural-language prompts (with governed data access).
Flag risks and opportunities so teams know what to do next.

AI-powered dashboards help users understand what changed, explore potential contributing factors, and uncover insights faster through AI-assisted analysis.

Why AI-powered dashboards matter in 2026

In 2026, organizations need analytics tools that do more than display historical data. With more data, more tools, and shorter decision windows, AI-powered dashboards help teams understand changes faster, reduce analyst dependency, and make confident decisions.

2026 analytics challenge	Why it matters	How AI-powered dashboards help
More data across more tools	Teams struggle to identify what needs attention across disconnected systems.	AI surfaces trends, anomalies, and key changes automatically.
Shorter decision windows	Delayed analysis can slow revenue, operations, and customer decisions.	Users can ask questions, explore data, and get answers faster.
Limited analyst capacity	Business teams often wait for analysts to explain metrics or build follow-up reports.	Natural language exploration helps users investigate insights independently.
Static dashboard limitations	Traditional dashboards show what happened but often do not explain why.	AI helps explain performance changes and highlight possible risks.
Growing self-service expectations	Users need easier ways to understand and act on data without technical support.	AI-powered dashboards turn dashboards into active decision-support tools.

These challenges explain why AI-powered dashboards are becoming more than a reporting upgrade. They help teams move from reviewing data to acting on insights. Here are the key use cases for AI-powered dashboards.

How different industries use AI-powered dashboards

AI-powered dashboards create value in different ways depending on the business function. The following examples show how teams use them to address specific challenges in sales, education, retail, and healthcare.

1. Improve sales forecasting

AI-powered dashboards analyze pipeline activity, revenue trends, order volume, and channel performance to identify risks and forecast future sales more accurately. This helps sales teams prioritize high-value opportunities and respond faster to changes in pipeline health.

AI-powered sales forecasting

Result: More accurate forecasts, better pipeline visibility, and faster revenue decisions.

2. Identify student performance risks earlier

AI-powered dashboards analyze enrollment trends, student performance, graduation rates, and academic outcomes to uncover performance gaps and emerging risks. This helps educational institutions take timely action and improve planning across departments or campuses.

Early student risk detection

Result: Earlier intervention, better academic visibility, and more informed planning.

3. Optimize inventory and merchandising

AI-powered dashboards analyze product sales, inventory movement, store performance, and customer behavior to identify demand patterns and purchasing trends. This helps retail teams reduce stockouts, avoid overstocking, and improve merchandising decisions.

Inventory and merchandising optimization

Result: Smarter inventory planning, better promotions, and faster retail decisions.

4. Improve healthcare operational visibility

AI-powered dashboards analyze patient intake, staffing levels, readmission trends, and departmental performance to identify operational pressure points. This helps healthcare administrators allocate resources more effectively and respond faster to changing operational demands.

Healthcare operations visibility

Result: Better operational visibility, smarter resource planning, and faster response times.

While AI-powered dashboards can help organizations make faster decisions, not every analytics platform delivers these capabilities in the same way.

Many BI tools provide AI features, but organizations often need more than AI-assisted analysis. They also need embedded analytics, conversational data exploration, self-service capabilities, and the flexibility to deliver insights directly within products and business workflows.

This is where Bold BI stands out by combining AI-powered analytics, embedded BI, and SaaS-ready capabilities within a single platform.

How Bold BI delivers AI‑powered dashboards

Organizations evaluating AI-powered dashboards often need more than AI-assisted analysis. They need a platform that combines AI-powered insights, embedded analytics, self-service capabilities, and flexible deployment options. Bold BI brings these capabilities together in a single solution.

1. AI-powered analytics and conversational insights

Many organizations struggle to turn dashboard data into actionable answers. Bold BI addresses this with AI Copilot, AI Agent, Smart Narrations, and Prompt-to-Dashboard capabilities that help users create visualizations, explore data, and understand trends using natural language.

They reduce the amount of manual exploration required by surfacing insights and enabling natural language analysis.

AI copilot in analytics

Smart narrations

Prompt-to-dashboard creation

Learn more about these AI-powered analytics features.

2. Embedded AI analytics for applications and SaaS products

Many BI platforms offer AI capabilities within standalone analytics environments. Bold BI enables organizations to embed dashboards and AI-powered analytics directly into customer portals, internal applications, and SaaS products.

This helps organizations bring analytics closer to where decisions happen, allowing users to access insights directly within the applications they already use.

Embedded AI analytics

Read: How to Embed AI Assistant in Your Software Application to learn more.

3. Unified AI Agent: Analyze data through conversational interfaces

Organizations often need answers that span multiple dashboards, reports, and datasets. Bold BI's Unified AI Agent provides a centralized conversational experience where users can analyze data, review dashboard summaries, explore insights, and interact with analytics from a single interface.

By bringing dashboard exploration, AI-generated insights, and data analysis together in one place, Unified AI Agent helps teams reduce context switching, accelerate discovery, and make faster, more informed decisions across the organization.

Unified AI agent

Watch our Talk to Your Data: Generative AI-Powered Dashboards in Bold BI [Webinar] for detailed insights.

4. Built for SaaS and multi-tenant environments

SaaS organizations need to deliver analytics securely across multiple customers while maintaining data isolation and tenant-level control.

Bold BI supports multi-tenant architecture, role-based access controls, and row-level security that help organizations deliver customer-facing analytics while maintaining security and governance.

Refer to our “Harnessing Multitenancy: Empowering Insights with Embedded BI” blog for more information.

5. Flexible deployment and enterprise readiness

Organizations have different infrastructure, security, and compliance requirements. Bold BI supports cloud and on-premises deployments, allowing teams to align analytics with their technology strategy.

This helps organizations scale analytics while maintaining control over data, users, and infrastructure.

Explore our “Choosing the Right BI Deployment Model: Cloud vs On-Premises“ blog for detailed insights.

Real-world use case of AI-powered dashboards

AI-InteleKt needed an analytics solution that could be embedded in its application and support customizable dashboards and self-service analytics. By adopting Bold BI, the company gained an easy-to-embed platform, simplified dashboard creation, and accelerated product delivery through a more integrated analytics experience. This demonstrates how organizations can combine AI-powered analytics, embedded dashboards, and self-service capabilities within a single platform.

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Ready to accelerate decision-making with AI-powered dashboards?

AI-powered dashboards are transforming analytics from passive reporting to active decision support. Instead of only showing what happened, they help teams understand performance changes, identify risks earlier, and decide what to do next.

For CTOs, CIOs, and product managers, this means less reliance on manual analysis, greater adoption of self-service analytics, and faster access to insights across teams and applications.

Bold BI® supports this shift by combining AI-powered analytics, embedded dashboards, conversational data exploration, and flexible deployment options in one platform. This helps organizations bring insights closer to everyday workflows and makes analytics a more practical part of decision-making.

Ready to accelerate decision-making with AI-powered dashboards?

Start a free trial.
Request a personalized AI-powered dashboards demo.

Related Resources

Frequently asked questions

1.
What are AI-powered dashboards?
AI-powered dashboards combine BI with AI techniques such as anomaly detection, forecasting, natural-language querying, and automated insight generation to help users understand and explore data more effectively.
2.
How do AI-powered dashboards differ from traditional dashboards?
AI-powered dashboards may include capabilities such as anomaly detection, natural language analysis, predictive insights, and AI-assisted recommendations, depending on the platform.
3.
Can AI-powered dashboards predict customer churn?
Yes. Many AI-powered dashboards use predictive analytics models to identify customers who may be at risk of leaving.
4.
Are AI-powered dashboards suitable for small businesses?
Yes. Many modern BI platforms provide AI-powered capabilities that are accessible to organizations of all sizes.
5.
What features should an AI-powered dashboard platform include?
Look for predictive analytics, natural language queries, automated insights, data integration, interactive dashboards, and strong governance capabilities.
6.
Why are AI-powered dashboards becoming important in 2026?
Organizations are managing larger volumes of data and making decisions in increasingly shorter timeframes. AI-powered dashboards help teams identify trends, understand performance changes, and access insights faster without relying heavily on manual analysis.

Integrate MySQL with Elasticsearch using Bold Data Hub

Bold BI by Syncfusion — Tue, 23 Jun 2026 11:23:56 +0000

TL;DR: Many organizations store transactional data in MySQL while search logs and event data live in Elasticsearch. Analyzing these datasets together is often difficult because they exist in separate systems. With Bold Data Hub, you can integrate MySQL and Elasticsearch into a unified dataset and build interactive dashboards in Bold BI® without writing complex data pipelines.

Introduction

By integrating MySQL and Elasticsearch sources, organizations can analyze operational data and search behavior together, creating a complete view of business performance.

Benefits of integrating MySQL and Elasticsearch data for analytics

Combining MySQL and Elasticsearch data in one dashboard provides the following benefits:

Better search and transaction analysis: By combining MySQL transactional data with Elasticsearch search logs, teams can analyze how users search for products and how those searches translate into purchases.
Unified analytics across operational and search data: MySQL stores structured business data such as orders and users, while Elasticsearch stores indexed search or event data. Integrating both allows dashboards to correlate operational metrics with user behavior.
Reduced manual data pipelines: Without integration tools, developers often export MySQL data and combine it with Elasticsearch using custom scripts. Bold Data Hub simplifies this by combining both sources in a single pipeline.
Build a performance monitor dashboard: Once the datasets are combined, you can create dashboards with real-time insights without writing custom scripts.

Challenges of integrating MySQL and Elasticsearch

Keeping both systems synchronized becomes difficult as data updates occur, causing inconsistencies between stored and indexed data.
Mapping and schema mismatches arise as structures differ, creating indexing issues and unpredictable search results.
Sync failures happen when pipelines break or delays occur, resulting in missing, outdated, or incomplete records.
Query translation is required because SQL logic doesn’t align with Elasticsearch, increasing development time and complexity.

How Bold Data Hub simplifies data integration

Unified data connectivity brings all your sources together, making it easy to connect databases, files, cloud apps, and APIs from one place.
Automated ETL workflows handle data movement from end to end, ensuring accurate and consistent processing without manual effort.
Powerful data transformation lets you clean, format, and reshape information using clear, customizable rules.
Centralized monitoring and management give full visibility into every pipeline, making performance and data quality easy to track.

Now that you understand how Bold Data Hub simplifies data integration, let’s explore how to connect MySQL and Elasticsearch to the Bold Data Hub.

Integrate MySQL with Elasticsearch data using Bold Data Hub

Bold Data Hub lets you easily combine MySQL and Elasticsearch data in one workspace, enabling unified visualizations and reducing manual preparation.

Step 1: Prepare the data sources

Before integration, ensure that:

MySQL database contains the required tables (for example: user data).
Elasticsearch contains relevant indexes (for example: feedback data).
Both datasets include a common field that can be used as a join key. [caption id="" align="alignnone" width="1484"] Sample data in MySQL[/caption]

[caption id="" align="alignnone" width="1484"] Sample data in MySQL[/caption]

This field allows datasets from MySQL and Elasticsearch to be linked together.

Step 2: Create a data pipeline in Bold Data Hub

Open Bold BI and launch Bold Data Hub. [caption id="" align="alignnone" width="1484"] Click the Bold Data Hub icon[/caption]
Next, select the plus (+) icon to create a pipeline. In this tutorial, the pipeline is named elasticsearch_mysql to represent the integration between Elasticsearch and MySQL. [caption id="" align="alignnone" width="1484"] Create the elasticsearch-mysql pipeline [/caption]
Add the MySQL data and configure it with your host, port, database, and credentials to extract the required tables. Example configuration:

version: 1.0.1

plugins:

extractors:

- name: mysql

connectorname: MySQL

config:

host: localhost

port: 3306

database: sales_db

username: root

password: ********

This configuration extracts transactional data from the MySQL database.

[caption id="" align="alignnone" width="1484"] Extract MySQl data in Bold Data Hub[/caption]

Step 3: Add Elasticsearch as a data source

Next, configure the Elasticsearch connector inside the same pipeline with your host, port, database, and credentials to extract the required tables.

Example configuration:

version: 1.0.1

plugins:

extractors:

name:     elastic_search

connectorname: Elasticsearch

config:

host: http://localhost:9200

index: product_feedback

username: elastic

password: ********

This allows the pipeline to retrieve search or event data stored in Elasticsearch.

[caption id="" align="alignnone" width="1484"] Add Elasticsearch data in Bold Data Hub[/caption]

Step 4: Save the datasets

After combining the MySQL and Elasticsearch datasets in the pipeline, select Save to store the unified configuration.

[caption id="" align="alignnone" width="1484"] Click the save button[/caption]

Once saved, a window will appear displaying the newly combined data source, confirming that the integration has been successfully created and is ready for use in the Data Source page.

[caption id="" align="alignnone" width="1484"] Data source listing page[/caption]

Joining tables

Step 1: Open the data source editor

Open the action menu for the created data source and select Edit Data Source.

[caption id="" align="alignnone" width="1484"] Edit the data source[/caption]

Step 2: Identify the common fields

The design area opens automatically. Drag and drop the tables from the left panel into the design area. In this case, we dragged products and sales_feedback tables.

[caption id="" align="alignnone" width="1484"] Identify common fields[/caption]

Step 3: Define the join condition

Once you drag and drop the second table into the designer, the query joiner window will open. In the Query Joiner window, define a join using a key.

Example:

mysql.products.product_id = elasticsearch.feedback_id

After configuring the join condition, select Update to apply the changes.

[caption id="" align="alignnone" width="1484"] Query Joiner window[/caption]

Step 4: Preview the combined datasets

Once the join is applied, the design page automatically displays a preview of the merged dataset. This allows you to verify that the data from both sources has been combined correctly.

[caption id="" align="alignnone" width="1484"] Preview of the combined datasets[/caption]

Next, let’s explore the key metrics you can build and visualize in the interactive dashboard.

How to create a dashboard using MySQL and Elasticsearch data

New users can begin by exploring our video on how to create a dashboard in Bold BI, which provides a quick overview of the dashboard-building process.

Total purchases.
Average render time.
Sales payment by type and browser.
Abandoned carts.
Abandoned revenue.
Products sold breakdown.
Page visits by traffic and device type.

These insights help organizations understand both customer intent and business outcomes. With Bold BI, you can easily transform the integrated dataset into interactive dashboards for decision-making.

[caption id="" align="alignnone" width="1484"] Preview of the dashboard[/caption]

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Start building unified analytics today

Frequently asked questions

1.
What data sources does Bold Data Hub support?

Bold Data Hub supports a wide range of data sources, including files, web APIs, SQL databases, and NoSQL systems. This includes popular options such as MySQL and Elasticsearch.
2.
Do I need any technical expertise to use Bold Data Hub?

No advanced technical expertise is required. A basic understanding of the data connectors you plan to use is sufficient to get started.
3.
Does Bold Data Hub offer additional features?

Yes. You can configure multiple datastores of the same server type and load data into them for different projects. It also includes an Open API connector and a Python connector for more advanced use cases.
4.
Do I need Bold Data Hub to use Bold BI?

No. Bold BI can connect directly to many supported data sources. Bold Data Hub is optional and is especially useful when you need to prepare, clean, or combine data, such as blending MySQL and Elasticsearch data, before building dashboards.

7 Best Looker Alternatives You Should Explore in 2026

Bold BI by Syncfusion — Mon, 22 Jun 2026 12:43:33 +0000

TL;DR: Looking for Looker alternatives in 2026? As analytics needs evolve, many teams evaluate the BI tools that support embedded analytics, customization, and different deployment models. This guide highlights seven options to consider and what teams often review when choosing a platform for their data, users, and business goals.

Introduction

Business intelligence today is increasingly focused on delivering fast, accessible, and actionable insights, not only on building dashboards. Looker is often used for governed, semantic modeling and analytics, including in data stacks built around SQL warehouses and LookML.

Depending on your deployment preferences, customization needs, embedded analytics requirements, or pricing considerations, you may want to evaluate alternative BI platforms. As analytics strategies continue to evolve, many teams in 2026 are exploring solutions that support different modeling approaches, deployment options, and workflow-oriented user experiences.

In this guide, we highlight seven Looker alternatives and outline the types of capabilities organizations commonly assess when selecting a business intelligence platform.

What to consider when choosing Looker alternatives

Choosing the right Looker alternative requires knowing which capabilities will help future‑proof your analytics stack. Here are the essential features to look for:

Effortless self-service features: Choose a platform that allows business users to explore data, apply filters, and build dashboards with minimal SQL or technical assistance for most common workflows.
AI‑assisted analytics: Look for solutions that offer AI‑assisted features, such as natural‑language querying, suggested visualizations, or automated summaries, where available, to help teams analyze data faster (capabilities may vary by edition and configuration).
Flexible embedded analytics: Select a BI tool that supports embedding dashboards, KPIs, and reports into your applications or SaaS products so insights fit directly into user workflows.
Easy and broad data integration: Ensure the platform offers connectors and APIs for cloud warehouses, SaaS tools, databases, spreadsheets, REST APIs, and (where needed) streaming sources to support unified reporting.
Tailored pricing: Choose a BI solution with transparent pricing that clearly outlines costs for connectors, embedding, or collaboration, helping reduce surprises as usage grows.
Faster setup and lower learning curve: Choose analytics tools with intuitive interfaces, drag‑and‑drop dashboards, and straightforward deployment options that can reduce onboarding time across teams.
Strong collaboration and governance: Look for built-in collaboration features like secure sharing, role-based access, and workspace governance to keep teams aligned and data protected.
Real-time, high-performance analytics: Select a platform that supports live data connections and fast dashboard rendering, with performance that can remain responsive as data volumes and concurrency grow (depending on data model and infrastructure).

Here are some leading embedded analytics platforms to consider in 2026.

7 best Looker alternatives you should explore in 2026

Many teams explore alternatives to Looker to find the right fit for their embedding requirements, modeling approach, deployment preferences, and budget. Below are seven platforms to consider in 2026.

1. Bold BI

Bold BI® is an embedded analytics platform used by teams that want to build, share, and embed dashboards inside apps or websites. Bold BI is often evaluated by organizations looking for scalable, customizable dashboards and configurable deployment options.

Key features of Bold BI:

Tailored pricing: Bold BI offers pricing options based on factors such as number of users and deployment type.
Embedding flexibility: Bold BI supports embedding across many popular frameworks, helping teams integrate analytics into existing web applications with control over UI, behavior, and user experience.
AI-assisted dashboard creation: Bold BI can generate dashboards using natural language, reducing the time spent on manual modeling and configuration so teams can move from questions to visuals faster.
Anonymous user embedding: Bold BI supports anonymous user embedding with group-based permissions, which can be useful for public portals, customer-facing apps, and scenarios where users aren’t required to use individual accounts.
Broader deployment options: Bold BI supports multiple deployment options, including Windows, Linux, Docker, Kubernetes, and Azure App Service.
Better dashboard navigation: Bold BI includes tab widgets that help organize complex dashboards into structured sections.
Live web connectors: Bold BI supports live web data connections, allowing dashboards to read from web-based sources in real time, where supported.
Custom email templates: Bold BI lets you create and manage custom email templates within the platform, supporting more consistent branding and better control over scheduled reports and notification workflows.

Explore our comparison page to learn more about why you should choose Bold BI over Looker.

2. Zoho Analytics

Zoho Analytics is a self-service BI platform known for intuitive dashboards, strong AI assistance, and broad data connectivity. It’s popular with SMBs thanks to its affordability and ease of use.

Key features of Zoho Analytics:

Easy drag-and-drop visualizations: Build dashboards quickly using 50+ chart types with a simple visual builder.
Built-in data preparation: Clean and transform data with native prep tools to improve reporting accuracy.
Automatic data blending: Combine data from multiple sources automatically for unified analysis.
Advanced AI with Zia: Ask questions in natural language, get automated insights, and detect anomalies faster.

3. Sisense

Sisense is an end-to-end analytics platform built for scalable, customizable embedded analytics. It excels at handling complex datasets and powering tailored analytics experiences.

Key features of Sisense:

Flexible pricing models: Choose pricing options that better match your scale, usage, and deployment needs.
Advanced customization across environments: Customize embedded analytics consistently across dev, staging, and production.
AI throughout the analytics lifecycle: Use AI across preparation, analysis, and insights to surface patterns faster.
Open, flexible data modeling: Adapt modeling to different data architectures for smoother integration and growth.

4. Domo

Domo is a cloud‑native BI platform offering end-to-end data integration, analytics, automation, and collaboration. It centralizes large, diverse data sources and supports deep embedding use cases.

Key features of Domo:

Native integrations: Connect to a broad library of prebuilt connectors across common business applications and data sources (availability varies).
Collaboration and alerts: Collaboration features and alerting options are available, depending on deployment and configuration.
Predictive AI capabilities: AI-assisted and predictive features are supported in certain scenarios.
Visualization options: Use a wide set of visualization types and dashboard design options.

5. Tableau

Tableau is a global leader in visual analytics that’s known for expressive, interactive dashboards and strong enterprise adoption. It’s ideal for teams that prioritize visual storytelling.

Key features of Tableau:

User satisfaction: Tableau consistently earns strong user satisfaction scores due to its easy-to-use interface and highly intuitive drag-and-drop dashboard creation.
Simpler implementation: Users frequently highlight Tableau’s smooth and straightforward setup process, making onboarding faster and less complex.
Data connectivity: Tableau offers wide-ranging support for diverse data sources, enabling seamless integration without limitations.
Rich visualizations: Tableau excels in interactive, dynamic, and rich data visualization, providing a more engaging analytics experience.

6. Qlik Sense

Qlik Sense uses an associative analytics engine that lets users explore data freely, moving beyond linear, SQL-based querying. This enables deeper, more flexible discovery.

Key features of Qlik Sense:

Data visualization: Qlik Sense delivers powerful and visually engaging dashboards, making it the stronger choice for teams that prioritize rich visual analytics.
Data preparation: Qlik Sense offers robust data preparation capabilities and broader connector support, enabling smoother integration across diverse data sources.
Real-time reporting: Qlik Sense provides real-time insights, helping organizations act on data more immediately.
Easier to use: Qlik Sense is generally easier for teams to adopt and navigate, offering an intuitive user experience.

7. ThoughtSpot

ThoughtSpot is redefining business intelligence with its Agentic Analytics Platform, which uses AI agents to deliver live, explainable insights directly into workflows. ThoughtSpot supports search-driven analytics and AI-assisted insights and is often evaluated by organizations looking to embed analytics and enable guided exploration across cloud data sources.

Key features of ThoughtSpot:

AI-powered search analytics: Ask complex questions in natural language and get instant, insight-rich answers.
AI-augmented dashboards: Explore dynamic dashboards that update automatically and support AI-assisted insights, depending on data connectivity and configuration.
Flexible data modeling: Model data with strong governance controls to maintain accuracy and consistency.
Unified analytics across platforms: Connect to cloud platforms and business apps without heavy ecosystem lock-in.

Why Bold BI is the right choice for you

Choosing a Looker alternative often depends on your embedding needs, integration requirements, deployment preferences, governance, and the level of customization required. Bold BI is designed to support embedded analytics scenarios along with configurable deployment and branding options. Feature availability, performance, and cost outcomes can vary based on data volume, usage patterns, and configuration.

Customer example:

Faster integrations without rebuilding workflows

Keyrenter needed stronger business analytics beyond what they were getting from AppFolio, especially for tracking property performance and operational work across markets. They chose Bold BI for its wide data integrations (including AppFolio) and the ability to personalize widgets for clearer market-level reporting.

Outcome: Faster rollout with smoother data connections.
How: Broad integrations and customizable widgets for tailored reporting.
Why it matters: Teams keep momentum and avoid slow, complex rebuilds.

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Get started with Bold BI® by signing up for a free trial and exploring its embedded analytics capabilities firsthand. With guided onboarding, comprehensive resources, and expert support, your team can start delivering insights faster, without a steep learning curve.

Want a closer look before committing? Request a personalized demo to see how Bold BI integrates with your tech stack, supports your deployment needs, and scales with your business. Whether you’re evaluating options or planning a migration, Bold BI makes it easy to take the next step with confidence.

Frequently asked questions

1.
What makes Bold BI a leading Looker alternative?
Bold BI supports embedded analytics, interactive dashboards, a broad range of data connectors, and configurable deployment options. Feature availability can vary by plan and deployment model.
2.
What can you build with Bold BI?
You can build interactive dashboards, KPI reports, embedded analytics views, and data-rich visualizations tailored to any business workflow.
3.
Do I need technical skills to use Bold BI?
No. Bold BI supports drag‑and‑drop dashboard creation, making it easy for non-technical users while still offering advanced options for developers.
4.
Can I share dashboards with my team?
Yes. Bold BI allows secure sharing, scheduling, and collaboration so teams can access insights in real time.
5.
Are Looker alternatives good for startups?
Yes. Many offer flexible pricing, quick setup, and scalable features suited for growing teams.

DEV Community: Bold BI by Syncfusion

What's New in v16.1.90 for Self-Service & Embedded Analytics

Introduction

AI Enhancements

Bring Your Own Key (BYOK) Support for Anthropic Claude Models

Improvements

Smarter Currency Formatting for AI-Generated Insights

Enhanced Dashboard Navigation Experience

Faster Dashboard Rendering Performance

Extended Duration Format Support

Keycloak SSO Token Storage Support for OpenID Provider

Other Bug Fixes and Minor Improvements

Real-World Use Cases Enabled by Bold BI v16.1.90

Adopt AI While Maintaining Control Over Provider Credentials

Improve Understanding of Financial Insights

Help Users Find Dashboards Faster

Accelerate Analytics Experiences for Large Dashboards

Simplify Analytics for Duration-Based Metrics

Strengthen Enterprise Authentication Workflows

Conclusion

How to Embed Dashboards in React with a Python Backend

Introduction

Why Embed Dashboards in React Applications

Why a Python Backend Is Essential

How to Embed a Dashboard in a React Application with Python

Prerequisites

Step 1: Get the React with Python Sample

Step 2: Embed Authentication in Bold BI

Step 3: Download the Embed Configuration file

Step 4: Add the Embed Configuration file

Step 5: Start the Python Backend

Step 6: Install React Dependencies

Step 7: Start the React Application

Step 8: View Dashboard Listing (Optional)

How the Sample Works

1. Dashboard Initialization in React

2. Token Generation

3. Dashboard Rendering

Verify the Dashboard

Best Practices for Production Deployment

Why Choose Bold BI for Embedding Dashboards in a React App with Python?

Frequently Asked Questions

Why use Django for embedding dashboards?

Can I embed dashboards without a backend?

What is tokenGenerationUrl?

Does Bold BI support React integration?

Is this architecture scalable?

Conversational Analytics in BI: Why It Matters in 2026

Introduction

What is conversational analytics?

Why organizations are adopting conversational analytics

Challenges to consider before adoption

Key features of conversational analytics

How conversational analytics differs from traditional BI and NLQ

Real-world use cases of conversational analytics

Sales performance analysis

Distributor delivery performance analysis

Financial and invoice analysis

How to evaluate conversational analytics platforms

How Bold BI supports conversational analytics

1. Ask questions in Natural Language with Unified AI Agent

2. Improve question understanding with AI Synonyms

3. Generate widgets from natural language

4. Create dashboards from a single prompt

5. Explain Insights with AI-Generated Narrations

Ready to make conversational analytics easier to explore?

Frequently asked questions

Integrate MySQL with Elasticsearch using Bold Data Hub

Introduction

Benefits of integrating MySQL and Elasticsearch data for analytics

Challenges of integrating MySQL and Elasticsearch

How Bold Data Hub simplifies data integration

Integrate MySQL with Elasticsearch data using Bold Data Hub

Step 1: Prepare the data sources

Step 2: Create a data pipeline in Bold Data Hub

Step 3: Add Elasticsearch as a data source

Step 4: Save the datasets

Joining tables

Step 1: Open the data source editor

Step 2: Identify the common fields