Top 5 self-service BI solutions for Clickhouse
Ambrus Pethes
Posted on November 20, 2024
ClickHouse is a fast open-source column-oriented database management system that allows the generation of analytical data reports in real time using SQL queries. Perfect for real-time analytics, ClickHouse lets you get your databases up and running in seconds—no need to worry about sizing, scaling, or upgrades! It also offers seamless scaling to adapt to your workload needs and robust security features that are SOC 2 Type II compliant, ensuring your data is safe and sound.
Integrating ClickHouse with business intelligence (BI) tools further enhances its capabilities, enabling users to create interactive dashboards and reports effortlessly. Multiple BI platforms integrate smoothly with ClickHouse, allowing analysts to visualize and examine their information without intricate configurations. In contrast, self-service BI goes a step beyond. This approach enables users to access, scrutinize, and illustrate data independently—eliminating the need for assistance from analysts or technical teams—thus facilitating swift and autonomous decision-making.
The alternatives can be categorized 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, Clickhouse. 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.
In this blog post, we will explore five top BI tools that work with ClickHouse, detailing how they can be connected and the unique features they offer to help you make the most of your data analysis efforts.
Top 5 self-service BI tools detailed comparison
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 Clickhouse?
As it is not a warehouse-native tool, you must use a third-party tool to connect your data to Clickhouse. This means you should copy your Amplitude data to the reverse ETL tool or use their built-in reverse tool to connect directly to Clickhouse.
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 Clickhouse: 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 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 Clickhouse?
https://clickhouse.com/docs/en/integrations/mitzu
It is a warehouse-native tool connected to ClickHouse, which means that instead of copying the company's product usage data, it generates native SQL queries over the company's data warehouse.
Pros
- Warehouse-Native connection to Clickhouse 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 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 Clickhouse?
As it is not a warehouse-native tool, you must use a third-party tool to connect your data to Clickhouse. This means you should copy your Mixpanel data to another tool directly connecting to Clickhouse.
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.
- Lack of native Clickhouse integration: Without direct connectivity to Clickhouse, maintaining data accuracy and timeliness becomes challenging. You'll need to set up and manage additional reverse ETL tools, potentially complicating your analytics workflow.
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 Clickhouse?
To connect a warehouse-native solution to ClickHouse, you must create a connection configuration by entering the ClickHouse server's hostname, port, username, password, and database name in the analytics platform. Then, you need to test and save the connection to use the data for analysis.
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. However, another layer of data warehousing must be introduced, such as PostgreSQL or Databricks, into your data stack as Clickhouse is not natively supported by Netspring.
- 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.
- Lack of warehouse-native connection to Clickhouse: Maintaining data accuracy and timeliness becomes challenging without direct integration. You'll need to set up and manage additional data pipelines, potentially complicating your analytics workflow.
Kubit
Kubit is an analytics platform that enables companies to gain valuable customer insights without the need to move their data into silos. This warehouse-native approach reduces ownership costs, conserves engineering resources, and provides more accurate and comprehensive self-service insights.
Pricing
Flat-pricing: Kubit charges you based on their given flat price. The price is based on Monthly Tracked Users (MTU), with tiers at 10K, 1M, and 5M users. Pricing changes according to these tiers.
Please note that Kubit does not officially publish this pricing information, so it may be outdated.
How do I connect to Clickhouse?
As a warehouse-native tool connected to ClickHouse, Kubit generates native SQL queries directly on the company's data warehouse instead of copying product usage data. This approach allows for more efficient and real-time analysis.
Pros
- Efficient Analytics: Kubit provides real-time insights and powerful analytics capabilities, enabling organizations to make data-driven decisions quickly.
- Quick Setup: Kubit can be set up rapidly, allowing businesses to start analyzing their data without extensive delays or complex configurations.
- Scalable Options: The platform is designed to scale with your business needs, accommodating growing data volumes and user bases without significant performance degradation.
- Collaboration Features: The platform includes tools for sharing insights across teams and facilitating collaboration between product, marketing, and data teams to drive informed decision-making.
Cons
- Lacks Integrations: Kubit may have limited integrations with other tools and platforms, which could hinder its ability to fit seamlessly into existing workflows.
- Data Caps for Flat Pricing: Some pricing tiers may impose data caps, restricting the amount of data that can be analyzed or stored, which could be a limitation for data-intensive organizations.
- Basic Features - limitations: While Kubit offers essential analytics functionalities, it may lack some advanced features in more established platforms, limiting its capabilities for sophisticated analysis.
- Steep learning Curve: Although designed for self-service, some users, particularly those not data-savvy, may still face a learning curve when trying to utilize all of Kubit's features effectively.
Conclusion
This page compares five self-service BI solutions for ClickHouse:
- Mitzu.io is a warehouse-native tool for ClickHouse that automatically generates SQL queries and offers subscription analytics. It shines in analyzing user journeys and looking up individual users. However, as a newer entrant, it might encounter scalability issues.
- Mixpanel is a powerful product analytics tool offering real-time insights and comprehensive data exploration. While it provides accessible analytics without SQL knowledge, its pricing model can be expensive for rapidly growing companies. However, Mixpanel requires additional reverse ETL tooling to work with Clickhouse.
- Netspring is a cutting-edge platform offering self-service analytics and SQL options. It provides rich product analytics reports but isn't warehouse-native for ClickHouse, requiring data export. While powerful, it may present a learning curve for non-technical users. However, Netspring requires additional data warehouse tooling to work with ClickHouse, such as PostgreSQL or Databricks.
- Kubit: A warehouse-native platform providing efficient analytics, quick setup, and scalable options. Despite its strengths in real-time insights, it may lack some advanced features and integrations compared to established competitors.
- Amplitude: A comprehensive product analytics platform known for its user-friendly interface and powerful behavioral analytics. It offers advanced user segmentation and predictive analytics but can be complex for beginners and potentially costly for larger organizations. However, Amplitude requires additional reverse ETL tooling to work with Clickhouse.
Each solution has its strengths and limitations, with varying pricing models and integration capabilities. The choice depends on specific business needs, technical expertise, and scalability requirements.
Posted on November 20, 2024
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