Top 5 self-service BI solutions for BigQuery

What is Google BigQuery?

Google BigQuery is a cloud service for processing large datasets, offering fast SQL-like queries and analytics. With its powerful architecture, BigQuery allows you to execute complex queries on massive datasets, ranging from terabytes to petabytes, without the need for extensive infrastructure management. This capability makes it an ideal solution for seeking actionable insights from your data while minimizing maintenance overhead. One of BigQuery's standout features is its ability to separate storage and compute resources, allowing for optimized performance and cost efficiency. You can benefit from its serverless model, which automatically scales resources based on workload demands, ensuring quick query responses without the hassle of provisioning servers.

BigQuery is an ideal data warehousing solution for small startups and large enterprises. It offers a generous free usage tier, while it can be cost-effective for large datasets as well.

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 BigQuery with business intelligence (BI) platforms significantly boosts its functionality, allowing users to craft interactive dashboards and reports effortlessly. Various BI tools connect seamlessly with BigQuery, 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

Self-service BI tools integrate perfectly well with BigQuery, leveraging its powerful data processing capabilities to deliver real-time insights and analytics. I have categorized the alternatives into two main groups:

• Third-party tools
• Warehouse-native self-service BI tools

Warehouse-native analytics solutions represent a new wave in the product and marketing analytics landscape. These tools operate directly on your existing data infrastructure, such as BigQuery, allowing cost-efficient and real-time access to first-party data. However, they require careful data modeling and optimization to ensure optimal performance in cloud data warehouses. This blog post will explore the top five self-service BI solutions that integrate seamlessly with BigQuery. I’ll detail how each tool connects to Databricks and highlight their unique features to help you maximize your data analysis efforts.

Top 5 self-service BI tools detailed comparison

Amplitude

Amplitude is an event-based analytics tool that tracks user behaviors based on in-product interactions and analyzes user behavior in real-time. Event-based analytics is the method of tracking and analyzing interactions between users and products, also known as events.

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 BigQuery?

You can set up recurring syncs of your Amplitude event data to BigQuery through the Amplitude UI or manually start a sync of your historical data. It is not a warehouse-native tool; you must use a third-party tool to connect your data to BigQuery. This means you should import your BigQuery data to the reverse ETL tool or use their built-in reverse tool to connect directly to BigQuery.

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 BigQuery: 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.

Mitzu.io

Mitzu.io is a no-code warehouse-native analytics platform designed specifically for product, marketing, and revenue analytics. 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 BigQuery?

By integrating directly with BigQuery, Mitzu removes the necessity for traditional reverse ETL processes, facilitating real-time analytics on existing data infrastructures. This method allows businesses to fully utilize their data without replicating it in various systems.

Connecting Mitzu and BigQuery simplifies the process for organizations to derive actionable insights from their product usage data.

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.

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 BigQuery?

With the recent release of Warehouse Connectors, integrating Google Cloud with Mixpanel has become significantly easier to set up and manage. This integration allows you to enrich your product and marketing data in Mixpanel with source-of-truth information from BigQuery, creating a comprehensive view that connects your product and marketing initiatives to measurable business outcomes. Mixpanel product data can also be centralized in BigQuery, providing even more value within the Google ecosystem. However, as it is not a warehouse-native tool you should import your BigQuery data to the reverse ETL tool or use their built-in reverse tool to connect directly to BigQuery.

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 BigQuery: 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 offers next-generation, warehouse-native Product and behavioral Analytics with the analytical power of Business Intelligence (BI). 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 BigQuery?

NetSpring on BigQuery allows customers to do advanced product analytics directly on top of a BigQuery data warehouse without any data movement. NetSpring is in the next generation of product analytics that is aligned with the modern data stack, centered on a central data warehouse store for all data. This warehouse-native approach improves 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.

PostHog

PostHog is an open-source product analytics platform that offers a wide range of tools for tracking user interactions, analyzing data, and gaining insights to enhance user experience and product performance. It provides event tracking, heatmaps, and feature flags, but its open-source and self-hosted nature is the standout feature.

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 BigQuery?

PostHog is not a warehouse-native tool, so you must use a third-party solution to connect your data to BigQuery. This requires you to sync your PostHog data through another ETL or reverse ETL tool.

Pros

• Open-Source: PostHog's open-source nature makes it highly customizable, allowing you to modify the platform to meet your specific requirements.
• Self-Hosted: The self-hosted option ensures data privacy and security control, which is crucial if your business has strict compliance needs.
• Comprehensive Feature Set: PostHog offers a wide range of features, including event tracking, session recordings, feature flags, heatmaps, and user cohorts, providing valuable insights into user behavior.
• Cost-Effective: Since it’s open-source, there are no licensing fees, making it an attractive option for small—to medium-sized businesses with budget constraints.
• Active Community: An engaged community supports ongoing updates, feature development, and user assistance.

Cons

• Steep Learning Curve: If you are unfamiliar with analytics platforms, you may initially find PostHog challenging to navigate, particularly when configuring advanced features.
• Resource-Intensive: Running PostHog as a self-hosted solution can require considerable hardware and technical expertise from your side, which might be difficult if you have a smaller organization.
• Limited Integrations: While PostHog integrates with popular tools, it is not warehouse-native, so you must always sync your data to BigQuery.
• Ongoing Maintenance: Self-hosted solutions require continual maintenance, updates, and monitoring, which could be burdensome if you have limited IT resources.

Conclusion

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

Mitzu.io: This, as a warehouse-native solution self-service BI tool, integrates smoothly with BigQuery, automatically generating SQL queries and offering subscription-based analytics. It is particularly effective in tracking user journeys and performing detailed lookups and cohort analyses. However, being a newer entrant in the market, it might face challenges related to scalability.

Mixpanel: A prominent player in the product analytics space, Mixpanel provides real-time insights and comprehensive data exploration features. It allows users to conduct analytics without requiring SQL knowledge, but its MTU-based pricing can be steep for fast-growing businesses. Moreover, effective integration with BigQuery necessitates additional reverse ETL tools.

Amplitude: This traditional product analytics platform is well-regarded for its intuitive interface and strong behavioral analytics capabilities. It offers features like advanced user segmentation and predictive analytics; however, its MTU-based pricing structure can be intricate for beginners and may become costly for larger enterprises. Like Mixpanel, Amplitude also requires supplementary reverse ETL tools for integration with BigQuery.

Netspring: A warehouse-native platform that combines self-service analytics with SQL capabilities. It generates detailed product analytics reports and operates directly on data within BigQuery, removing the need for data duplication. While it has powerful functionalities, Netspring may pose challenges for users who are not technically inclined. Also, we still do not know what will happen with their strategy after being acquired.

Posthog: PostHog is not a warehouse-native tool, so you'll need to use a third-party solution to connect your data to BigQuery, requiring you to sync your PostHog data through another ETL or reverse ETL tool. While PostHog offers valuable features like open-source customization and in-app guidance, potential users should consider the complexities of integrating it with data warehouses and the associated maintenance challenges.

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