Top 5 self-service BI solutions for Databricks

What is Databricks?

Databricks is a unified data analytics and engineering platform for enterprises of all scales. It connects easily with cloud storage and manages cloud infrastructure for users. In the Databricks workspace, you can access a comprehensive tool suite for various data-related tasks. This includes efficiently managing ETL processes, designing insightful dashboards, implementing robust security and governance measures (unity catalog), exploring in-depth data, and managing machine learning models.

At its core, the innovative data lakehouse concept transforms data management by seamlessly combining the strengths of enterprise data warehouses and data lakes, providing a unified and powerful solution for modern data challenges.

Why use self-service BI?

Traditional BI platforms often necessitate considerable technical expertise, which can result in data bottlenecks and delays in decision-making. It also focuses on strict control over data, limiting access to a small group of experts with the technical skills to use them effectively. This can create bottlenecks, as non-technical users often struggle to get the necessary insights.

Self-service BI platforms empower end business users—those without technical backgrounds—to analyze data and create visualizations independently, without relying on technical teams. These platforms prioritize broad access to data, making it available to as many people as possible. By putting data at users' fingertips, self-service BI empowers everyone in the organization to analyze and visualize information independently.

Integrating Databricks with business intelligence (BI) platforms significantly boosts its functionality, allowing users to craft interactive dashboards and reports effortlessly. Various BI tools connect seamlessly with Databricks, enabling analysts to visualize and analyze their data with minimal setup. On the other hand, self-service BI takes this a step further by empowering users to independently access, investigate, and present data.

Type of self-service BI tools

Integrating Databricks with Self-service BI empowers you to access, analyze, and illustrate data independently, eliminating dependency on technical teams. I have categorized the alternatives into two groups.

• Third-party applications
• Warehouse-native self-service analytics tools

Warehouse-native analytics solutions are a recent addition to the product and marketing analytics market. They operate directly on your existing data infrastructure, such as data warehouses—in this case, Databricks. These solutions offer two main advantages: cost efficiency and real-time access to first-party data. However, their primary drawback is the need for careful data modeling and optimization to ensure swift performance in cloud data warehouses.

This blog post will explore the top five self-service BI solutions that work exceptionally well with Databricks. We’ll detail how each tool connects to Databricks and highlight the unique features they offer to help you maximize your data analysis efforts.

Top 5 self-service BI tools detailed comparison

Mitzu.io

Mitzu.io is a no-code warehouse-native product, marketing, and revenue analytics platform. Like other warehouse-native tools, it enables users to query product usage data without knowledge of SQL or Python.

Pricing

Seat-based: This model charges based on the number of user seats or licenses allocated to an organization's individuals. Each seat typically corresponds to a specific user who can access the software, regardless of how often they use it.

How do I connect to Databricks?

Mitzu.io's warehouse-native approach enables seamless integration with Databricks, offering a powerful combination of pre-built product analytics visualizations and BI-style data exploration capabilities. By directly querying your Databricks data warehouse, Mitzu.io eliminates the need for data duplication, providing real-time access to all your enterprise data for comprehensive analytics. Mitzu supports all Databricks SQL Warehouse and cluster types. The recommended engine is Serverless SQL Warehouse or PRO SQL Warehouse.

Pros

• Warehouse-Native Analytics with Automatic SQL Query Generation: It simplifies data analysis by merging product data with marketing and revenue insights directly from your data warehouse. It automatically generates SQL queries based on your inputs, so you don’t need extensive SQL knowledge to get valuable insights.
• User Journey, Funnel, and Retention Analysis: You can track user interactions across various touchpoints to gain insights into their journey, conversion rates, and engagement, helping you improve retention strategies and keep users engaged.
• Individual User Lookup, Segmentation and Cohort Analysis: It analyzes user behavior by creating cohorts based on pricing plans, company size, and location for a more tailored approach. It allows for targeted analysis and personalized strategies.
• Subscription Analytics (MRR, Subscribers): Mitzu.io stands out as the only tool among its competitors that can handle subscription analytics, providing you with insights into Monthly Recurring Revenue (MRR) and subscriber metrics.
• Coverage of supported types: It’s important to see what data types they can handle for warehouse-native applications. Mitzu also supports Arrays, Tulips, and the brand-new JSON type.

Cons

• Limited Brand Recognition: As a newer player in the analytics market, Mitzu.io may lack the brand recognition and trust that established competitors like Amplitude and Mixpanel have built over the years.
• Scalability Concerns: Mitzu.io may face challenges in scaling its infrastructure and support as its user base grows. This could impact performance and customer service responsiveness, particularly for larger organizations with complex data needs.
• No AI tool: Mitzu stands out with its no-AI approach—it doesn't rely on artificial intelligence to generate insights. This commitment allows users to trust the accuracy and transparency of their data, ensuring that all analyses are based on real, unaltered information.

Amplitude

Amplitude is a leading product analytics platform that helps organizations transform raw user data into actionable insights. Amplitude provides a comprehensive view of how users interact with digital products by tracking user behavior and understanding customer journeys.

Pricing

MTU-based: MTU-based pricing charges organizations based on the number of unique users actively engaging with the product within a given month.

How do I connect to Databricks?

Amplitude's Databricks import source enables you to import data from Databricks to your Amplitude account. Databricks import uses the Databricks Change Data Feed feature to access and extract live data from your Databricks workspace securely. However, as it is not a warehouse-native tool, you must use a third-party tool to connect your data to Databricks. This means you should copy your Databricks data to the reverse ETL tool or use their built-in reverse tool to connect directly to Databricks.

Pros

• Comprehensive Product Analytics: Amplitude is designed to help you turn raw user data into meaningful insights. Features like real-time analytics, user segmentation, retention analysis, and conversion tracking provide a holistic view of how users interact with your digital products.
• User-Friendly Interface: The platform offers an intuitive interface that makes it easy to analyze user behavior and understand customer journeys.
• Advanced Cohort Analysis and A/B Testing: Amplitude shines in cohort analysis, allowing you to segment users based on their behaviors. Its built-in A/B testing feature also enables you to experiment with different strategies to optimize marketing outcomes efficiently.

Cons

• High Costs: One significant drawback is Amplitude’s event-based pricing model, which can become expensive as your product scales. Companies often pay for unused events, and as their Monthly Tracked Users (MTU) grow, you receive the same features at a higher price.
• Complex Setup and Maintenance: Implementing Amplitude requires extensive planning and manual event tagging. This process can be time-consuming and resource-intensive, hindering your ability to respond quickly to changing business needs.
• Data Moving Challenges: Since Amplitude is a vertically integrated SaaS application focused on product-related event data, users often need to engage in time-consuming reverse ETL processes to analyze the complete customer journey. This can lead to fragmented analytics and a lack of holistic insights.
• No warehouse-native connection to Databricks: Without a native integration, you may face challenges in maintaining data accuracy and timeliness, as you need to set up and manage additional data pipelines.

Pendo

Pendo is a product analytics tool that enables you to create improved software experiences that lead to happier and more productive users and employees. Pendo combines powerful software usage analytics with in-app guidance and user feedback capabilities, enabling even non-technical teams to deliver better product experiences to their customers or employees.

Pricing

MAU-based: MAU-based pricing charges organizations based on the number of unique users actively engaging with the product within a month.

How do I connect to Databricks?

To connect Pendo data to Databricks, you must utilize a third-party ETL or reverse ETL tool, as Pendo does not offer native integration. This requires setting up additional data pipelines to ensure accurate and timely data synchronization between Pendo and Databricks.

Pros

• Comprehensive Product Insights: Pendo provides in-depth analytics that allows you to track user behavior across their applications.
• Integrated In-App Guidance: The platform enables you to create in-app messages and guides without coding, facilitating user onboarding and feature adoption.
• Robust Feedback Mechanisms: It includes tools for collecting user feedback through surveys and polls, allowing you to capture sentiment and insights directly from your users at crucial moments in their journey.
• Powerful Session Replay: The session replay functionality allows you to visualize user interactions within the app to find real customer feedback.
• Strong Community Support: Pendo is backed by an active community and resources like Mind the Product, offering training, events, and content to help product managers and teams improve their skills and knowledge.

Cons

• High Cost: Pendo's pricing can be steep, especially for small businesses or startups. As companies scale, the costs may become really high as they rely on MAU.
• Complex Setup and Learning Curve: While Pendo offers many features, setting them up can be complicated. New users may find it challenging to navigate the platform effectively, leading to a steep learning curve.
• Customization Challenges: Although Pendo is designed to be user-friendly, customizing the platform to meet specific business needs can be complex and may require technical expertise.
• Potential for Feature Bloat: As Pendo continues to add new features, there is a risk of feature bloat where additional functionalities may overshadow core capabilities, potentially complicating your experience.
• No warehouse-native connection to Databricks: Without a native integration, you may face challenges in maintaining data accuracy and timeliness, as you need to set up and manage additional data pipelines.

Mixpanel

Mixpanel is a straightforward yet powerful traditional product analytics tool that enables product teams to track and analyze in-app engagement effectively. It provides a clear view of every moment in the customer experience, allowing you to make informed changes that enhance user satisfaction.

Pricing

MTU-based: MTU-based pricing charges organizations based on the number of unique users actively engaging with the product within a given month.

How do I connect to Databricks?

Since Mixpanel isn't a warehouse-native tool, you'll need to employ a third-party solution to link your data to it. You can establish recurring syncs from Databricks to ensure that Mixpanel remains consistently updated with your trusted data. This integration allows you to import various types of data, including event data, user profiles, group profiles, and lookup tables. This process involves either copying your Databricks data to a reverse ETL tool or utilizing the built-in reverse ETL functionality provided by these tools to establish a direct connection with Databricks.

Pros

• No SQL Required: One of Mixpanel's standout features is its ability to explore data without SQL expertise. This accessibility allows you to easily set up metrics and analyze data without extensive technical training.
• Real-Time Insights: It provides live updates on user interactions, enabling teams to adapt and optimize their products based on current user behavior.
• Comprehensive Data Exploration: Mixpanel offers powerful data analysis capabilities, allowing you to dissect information and uncover meaningful trends and patterns effectively. These insights directly inform your product strategy. The platform's feature for setting up growth and retention metrics enhances your strategic planning process.

Cons

• High Cost: Mixpanel’s pricing model is a significant drawback, as it can become quite expensive as your business scales. While it offers a free tier, charges are based on monthly recurring revenue (MRR), potentially leading to steep costs for rapidly growing companies.
• Limited User Journey Features: Mixpanel may not be the best fit if your needs include guiding users through product features using behavior-driven triggers. Its focus is primarily on analytics rather than user onboarding.
• Insufficient Advanced Segmentation: The platform's segmentation capabilities may not be robust enough for organizations requiring more complex analytical frameworks. This limitation could hinder detailed insights into user behavior.
• No warehouse-native connection to Databricks: Without a native integration, you may face challenges in maintaining data accuracy and timeliness, as you need to set up and manage additional data pipelines.

Netspring

NetSpring is a next-generation Product and Customer Journey Analytics SaaS platform. It helps product-led companies better understand product usage and customer behavior to optimize growth metrics—from acquisition to revenue. NetSpring works securely on customers' data warehouses, bringing BI's ad hoc exploratory power to traditional templated product analytics.

Pricing

Seat-based: This model charges based on the number of user seats or licenses allocated to an organization's individuals. Each seat typically corresponds to a specific user who can access the software, regardless of how often they use it.

How do I connect to Databricks?

It is a warehouse-native tool integrated with Databricks, meaning it generates native SQL queries directly over the company's data warehouse instead of copying product usage data. This approach enhances efficiency by providing real-time access to insights without the overhead of data duplication, enabling faster and more informed decision-making.

Pros

• Self-Service: Access a rich library of product analytics reports and easily switch between reports and ad hoc visual data exploration to find answers to your questions.
• Warehouse-Native: Integrate product instrumentation with any business data in your data warehouse for comprehensive, context-rich analysis.
• SQL Option: This option simplifies funnel and path queries without requiring complex SQL while still allowing for the use of SQL for specialized analyses.
• Product and Customer Analytics: Utilize solutions for behavioral analytics, marketing analytics, operational analytics, customer 360 views, product 360 insights, and SaaS product-led growth (PLG) strategies.

Cons

• Limited Brand Recognition: As a newer entrant like Mitzu.io in the analytics market, NetSpring may lack the brand trust and recognition that established competitors possess, which could deter some potential customers. Optimizely has also acquired it, so the future strategy is still unknown.
• Learning Curve for Non-Technical Users: While NetSpring is designed for self-service, users without technical backgrounds may still face challenges in fully utilizing its features.
• Feature Limitations Compared to Established Competitors: While offering essential analytics capabilities, NetSpring may not have as many advanced features or integrations as the other platforms.

Conclusion

In this blog, I compared five self-service BI solutions for Databricks:

Mitzu.io: It is a warehouse-native tool seamlessly integrated with Databricks, automatically generating SQL queries and providing subscription analytics. It excels in analyzing user journeys and performing individual user lookups and cohorts. However, as a newer entrant in the market, it may face scalability challenges.

Mixpanel: A well-known traditional product analytics platform that delivers real-time insights and extensive data exploration capabilities. While it allows users to conduct accessible analytics without requiring SQL expertise, its MTU-based pricing model can be high for rapidly growing companies. Additionally, Mixpanel necessitates extra reverse ETL tooling to integrate effectively with Databricks.

Amplitude is a traditional product analytics solution known for its user-friendly interface and robust behavioral analytics capabilities. It offers advanced user segmentation and predictive analytics; however, its MTU-based pricing can be complex for beginners and potentially costly for larger organizations. Like Mixpanel, Amplitude also requires additional reverse ETL tooling to work with Databricks.

Netspring: A warehouse-native platform that offers self-service analytics alongside SQL options. It provides detailed product analytics reports and operates directly on data within Databricks, eliminating the need for data duplication. Although it has powerful features, Netspring may pose a learning curve for non-technical users.

Pendo: It offers valuable insights and features for improving product experiences; potential users should consider its high costs and the need for additional tools to integrate with data warehouses when evaluating its fit for your organization.

Each solution presents unique strengths and limitations, with varying pricing models and integration capabilities with Databricks. The optimal choice will depend on your specific business needs, technical expertise, and scalability requirements.