Top 5 Cloud Data Warehouse Vendors for Business Intelligence

TL;DR: As BI adoption grows, teams often face a practical challenge, not with visualization, but with how analytics data is prepared and organized before dashboards are built. Data may come from multiple databases, applications, or SaaS tools, making it harder to reuse datasets, maintain consistent metrics, and support analytics at scale. That’s why many teams evaluate cloud data warehouse vendors as part of their BI stack.

A cloud data warehouse acts as a centralized analytics data layer that BI tools connect to. In this blog, we compare five popular cloud data warehouse platforms and explain how Bold BI® works with them to help you build dashboards.

Introduction

Most analytics journeys start simple. Teams build dashboards by connecting directly to databases, APIs, or SaaS tools. For early use cases, this approach works well and delivers quick results. As analytics adoption grows, teams face a new challenge, not with visualization, but with how analytics data is prepared and reused. Data comes from many systems, similar datasets are rebuilt for each dashboard, and maintaining consistency across reports becomes harder over time.

A cloud data warehouse is designed to support BI workloads by consolidating data from multiple systems into a single platform optimized for reporting and scale. But choosing the right warehouse and using it effectively with a BI tool requires understanding how it performs in real-world dashboard usage.

In this blog, you’ll learn what a cloud data warehouse is in BI terms, why you need it, the common pitfalls that make vendor selection risky, and the practical criteria BI teams should use to compare options. You’ll also see how Bold BI connects to cloud data warehouses to operationalize dashboard delivery, helping teams standardize KPIs, control access, and manage refresh strategies as analytics usage grows.

Who Is This Blog For?

This guide is for teams involved in selecting and operationalizing a cloud data warehouse for modern BI, including:

• BI developers and BI leads who build dashboards and need a cloud data warehouse that supports consistent KPIs, stable refreshes, and concurrency at scale.
• Data engineers and analytics engineers responsible for preparing analytics-ready data models and ensuring cloud data warehouse performance, governance, and cost controls.
• Data and IT decision-makers (analytics managers, architects, platform owners) comparing vendors and needing clarity on trade-offs—and how Bold BI connects to each for production BI delivery.

Now that the audience is clear, let’s define what a cloud data warehouse is in BI terms.

What Is a Cloud Data Warehouse?

A cloud data warehouse is a central place in the cloud where organizations store data from across their systems so they can analyze it quickly and reliably for dashboards and decision-making. BI tools extract and analyze data from the warehouse to produce actionable insights.

Next, let’s look at why you need a cloud data warehouse specifically for modern BI dashboards.

Why Modern BI Needs a Cloud Data Warehouse

Modern BI demands more than data storage; it requires a foundation that can deliver consistent, scalable, and governed analytics as dashboard usage grows. That’s why teams use a cloud data warehouse to:

1. Keep dashboards trustworthy: Centralizing analytics-ready data reduces KPI drift and “two versions of the truth” across teams.
2. Handle heavy BI query loads: Warehouses are optimized for analytical workloads (joins, aggregations, large scans) that operational databases aren’t built for.
3. Scale under concurrency: Modern BI often means many users viewing dashboards at once—cloud warehouses are designed to handle concurrent reads reliably.
4. Improve refresh reliability: Governed datasets and stable models make scheduled refreshes less fragile as pipelines and schemas evolve.
5. Support governance and compliance: Role-based access, auditing, and policy enforcement are easier when analytics data is centralized.
6. Protect operational systems: Warehouses isolate reporting workloads so dashboards don’t slow down transactional apps like CRM or ERP.
7. Control costs as adoption grows: Workload controls and usage visibility help manage analytics spend as traffic and refresh frequency increase.

The next section outlines six practical tips to help you select a cloud data warehouse vendor that avoids these pitfalls and supports reliable BI delivery.

6 Tips for Selecting the Best Cloud Data Warehouse Vendor for BI

Before choosing the best cloud data warehouse for BI, evaluate it based on how well it supports production dashboards, not just data storage:

1. Security, privacy, and governance: Choose a vendor that supports encryption (at rest and in transit), role-based access control, auditing, and identity integration. Strong governance ensures sensitive data stays protected and dashboard access remains controlled as usage scales.
2. Data quality and consistency: Look for features that support stable schemas, metadata visibility, and compatibility with data validation and transformation workflows. Strong data quality controls reduce KPI mismatches, broken joins, and refresh failures that undermine trust in dashboards.
3. Performance optimization for BI workloads: Prioritize platforms designed for analytical query patterns like aggregations, joins, and frequent reads. Features like query optimization, caching, and workload management help keep dashboards responsive, even during heavy usage.
4. Scalability and concurrency handling: Ensure the warehouse can scale compute smoothly and handle multiple users accessing dashboards at the same time. Concurrency performance is critical for BI, where dashboards may be opened simultaneously by many users.
5. Cost management and predictability: Evaluate pricing transparency and workload controls such as resource limits, autoscaling policies, and usage visibility. The right platform allows BI adoption to grow without unexpected cost spikes.
6. Accessibility and adoption for BI teams: Select a vendor that works well with BI tools, one that offers reliable connectors, standard SQL support, and easy monitoring. Higher accessibility reduces operational overhead and helps BI teams deliver dashboards faster and more reliably.

With these criteria in mind, here’s how five commonly evaluated cloud data warehouse vendors compare for modern BI workloads.

Top 5 Cloud Data Warehouse Vendors for Modern BI

Here’s how these vendors map to those criteria.

1. Snowflake

Snowflake is a cloud data warehouse built for centralized analytics with independent scaling of compute and storage. This separation of compute and storage allows you to scale dashboard performance and data capacity independently, without re-architecting your datasets.

• Ease of use: Snowflake provides a streamlined experience for managing analytics data and running SQL queries, helping BI developers move from connection to dashboard faster with less platform overhead.
• Data management: It supports efficient analytics data organization with features that simplify data handling and reduce manual maintenance as datasets grow.
• Data integration: Snowflake connects cleanly with BI tools and data pipelines, making it easier to build datasets and power dashboards without complex integration effort.

For a step-by-step guide on how to connect your Snowflake data to Bold BI, refer to this documentation.

2. Databricks

Databricks is a data intelligence platform used for analytics, data engineering, and large-scale processing (often as part of a lakehouse approach). It combines scalable compute with a lakehouse approach, letting teams process and structure data in one platform before serving it to BI tools for reporting.

• Ease of use: Choose Databricks when you want a unified workspace that supports collaborative development and accelerates analytics workflows across data engineering and BI use cases.
• Data management: It supports managing and processing large datasets through scalable compute and structured workflow capabilities, helping teams prepare analytics-ready data efficiently.
• Data integration: Databricks integrates with major cloud services and data tools, making it easier to connect pipelines, storage layers, and BI environments across platforms.

Bold BI supports connecting Databricks through Azure. For a step-by-step guide on setting up the connection, refer to this documentation.

3. Amazon Redshift

Amazon Redshift is an analytics warehouse designed for SQL-based BI workloads within the AWS ecosystem. It runs as a managed warehouse in AWS, using clusters or more elastic options depending on configuration, so performance and cost depend heavily on workload setup and scaling choices.

• Ease of use: It provides a familiar SQL-based experience for building analytics datasets and dashboards, especially for teams already comfortable with AWS tooling.
• Data management: It supports managing large datasets and running analytical queries efficiently when data models and query patterns are designed well.
• Data integration: It integrates closely with AWS services, helping simplify ingestion and analytics workflows within a single cloud environment.
• Scaling decisions: Capacity choices and workload distribution can affect cost predictability and dashboard responsiveness as usage grows.

To connect your Amazon Redshift data with Bold BI, follow the steps provided in this documentation.

4. Azure Synapse Analytics

Azure Synapse Analytics combines warehousing, serverless querying, and big data processing within one Azure workspace. With it, teams can run SQL queries, Spark jobs, and pipeline workflows without switching platforms.

• Ease of use: Azure Synapse provides a unified workspace with a simple GUI, centralized user management, and support for SQL, notebooks, and Spark jobs, reducing context switching for BI developers.
• Data management: It offers end-to-end managed infrastructure where connections, workloads, and maintenance are handled within Synapse, simplifying how analytics data is stored, queried, and governed.
• Data integration: Synapse integrates tightly with Azure services such as Azure Data Factory, Azure Databricks, Power BI, and Git, enabling smoother data ingestion, transformation, and analytics workflows within Azure.

Check out this documentation for a step-by-step guide on how to connect an Azure Synapse Analytics database with Bold BI.

5. Google BigQuery

Google BigQuery is a fully managed cloud data warehouse designed for fast SQL analytics on large datasets in Google Cloud.

• Ease of use: BigQuery supports standard SQL and a straightforward query experience, helping BI developers analyze large datasets without managing servers or complex infrastructure.
• Data management: Its managed architecture and query-optimized storage help teams process and analyze high-volume data efficiently, especially for analytics-heavy workloads.
• Data integration: BigQuery aligns well with Google Cloud services and common data pipelines, making it easier to move data in and connect BI tools for dashboarding.

To connect your Google BigQuery data with Bold BI, follow the steps provided in our documentation.

Think of a warehouse as the data foundation. Bold BI serves as the delivery layer, turning governed data into consistent dashboards and embedded analytics with controlled access and reliable refresh strategies.

Next, let’s see how Bold BI makes your selected warehouse usable for real-world dashboard delivery.

How Bold BI Works With Cloud Data Warehouse Vendors

Once you choose a cloud data warehouse, the next make-or-break step is BI delivery in production—keeping dashboards fast and consistent without refresh failures, governance gaps, or runaway costs when usage scales.

Bold BI sits on top of your warehouse. You don’t need to move or reprocess data. Bold BI connects to the governed, analytics-ready models already in your warehouse and turns them into dashboards and embedded analytics.

• Centralized connections: Connect Bold BI to your warehouse (and other supported sources) so dashboards query a single source of truth instead of pulling from multiple operational systems.
• Reusable data models: Build joins, calculated fields, and KPI definitions once and reuse them across dashboards, so “Revenue” or “Active Users” doesn’t mean something different in every report.
• Built-in security and governance: Enforce role-based access at the dataset and dashboard level, so the right users see the right data without duplicating datasets or creating one-off workarounds.
• Flexible refresh control: Use live mode when freshness matters, or schedule refreshes when you need predictable load and stable dashboard behavior.
• Seamless embedding: Embed dashboards into your portals and apps with controlled access, so internal and external users can explore insights securely.
• Smart query strategies (live vs. extract modes): Use live mode for near real-time monitoring and operational visibility. Use extract mode for high-traffic dashboards to reduce repeated warehouse queries, stabilize performance under concurrency, and keep costs predictable as adoption grows.

Explore Bold BI’s cloud data warehouse connectors to see its supported authentication and connection options. Also, refer to our blog post “Data Warehouse and Data Mart: Recommendations & Uses” to learn how to connect Bold BI with a data warehouse.

Real-Time Data Warehouse Dashboards in Bold BI

These data warehouse dashboard examples show how connecting a cloud data warehouse with a BI layer like Bold BI helps deliver fast, reliable dashboards while keeping performance, governance, and cost under control.

1. Snowflake usage overview dashboard

The Snowflake usage overview dashboard presents key data warehouse metrics, including total queries (successful vs. failed), average elapsed time, query success rate, credits used, and cost by warehouse, in a single, consolidated view. This makes it easier to spot performance bottlenecks, identify which warehouses drive spend, and make faster decisions about optimization as concurrency grows.

2. Amazon Redshift sales activity tracker dashboard

By integrating Amazon Redshift data, you can create a sales activity tracker dashboard that consolidates revenue trends, customer activity, and sales performance into a single analytics source. This makes it easier to compare results across time periods or segments, spot shifts early, and take action using interactive dashboards instead of manual exports.

Final Thoughts

Choosing the best cloud data warehouse for BI matters, but results depend on how well you deliver that data.  Even with a strong warehouse, teams can still face inconsistent KPIs, governance gaps, and rising query costs as usage grows, unless the BI delivery layer standardizes definitions, access control, and refresh strategies. By pairing a cloud data warehouse with a BI and embedded analytics platform like Bold BI®, developers can turn governed data into fast, reliable dashboards and embedded analytics.

With native warehouse connectors, reusable models, role-based access control, live mode, and extract mode, Bold BI helps you scale analytics delivery without overloading your data warehouse or operational systems. Whether you’re evaluating Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, or Databricks, Bold BI ensures your analytics stay consistent, performant, and easy to share as dashboard needs grow.

Next Steps

• Explore Bold BI’s connectors for Snowflake, Redshift, BigQuery, Synapse, and Databricks.
• Learn when to use live mode and extract mode for performance and cost control.
• View sample dashboards built on cloud data warehouses.

Ready to get started? Try Bold BI for free and connect it to your cloud data warehouse to build, share, and embed dashboards with confidence.

Frequently Asked Questions

1. Is Bold BI a data warehouse?

   No. Bold BI is an AI and embedded analytics platform. It connects to data warehouses and analytics platforms to visualize data, but it does not replace the warehouse.

2. Can cloud data warehouses handle large data volumes?

   Yes. They are built to scale as data grows.

3. Can Bold BI connect to multiple cloud data warehouses?

   Yes. Bold BI supports connections to several popular cloud data warehouse platforms.

4. Are cloud data warehouses secure?

   Most vendors offer encryption, access controls, and compliance features.