How Does Mitzu Work?

Mitzu.io is a warehouse-native product analytics application offering features designed for in-depth product data analysis directly over data warehouses or data lake.

High level features of Mitzu.io include:

Self-Served Product Analytics: Mitzu.io allows users to perform product analytics directly over their data warehouse tables, enabling teams to access and analyze data independently without requiring specialized data skills like SQL or Python knowledge.
SQL Native Approach: The platform operates on a SQL-native basis, meaning that it generates SQL queries from user questions without copying any data away from the data warehouse. This ensures that data remains secure and analyses are performed on the most current available data. This approach minimizes the cost of product analytics compared to traditional product analytics.
Easy Integration: Mitzu.io boasts a quick and straightforward integration process, taking as little as five minutes to connect to all major data warehouse and data lake solutions. This ease of integration facilitates rapid deployment and minimizes setup time, allowing teams to start analyzing their product data without delay.

These features make Mitzu.io a practical and efficient tool for organizations looking to leverage their existing data warehouse infrastructure for product analytics, ensuring data security and supporting data-driven decision-making processes.

The SQL Native Approach

SQL Native product analytics platforms directly interact with your data warehouse, generating SQL queries based on user questions or actions. In contrast, traditional product analytics (Mixpanel or Amplitude) require a copy of your data.

Comparing SQL Native approach to Traditional product analytics

SQL native product analytics	Traditional product analytics
✅ Works on up to date data	❌Requires slow and expensive data copy
✅ Can manage dimension tables	❌Can't ingest dimension tables
✅Affordable pricing (seat or insight based)	❌Montly tracked user or events based pricing
✅Easy integration to your warehouse (5 minutes)	❌Extra tool is often needed for ReverseETL
❌Not suitable for small startups without a data warehouse	✅Easy to get started with small startups
❌Performance depends on the data warehouse usage and cluster size.	✅Relatively consistent performance

SQL native product analytics

Traditional product analytics

✅ Works on up to date data

❌Requires slow and expensive data copy

✅ Can manage dimension tables

❌Can't ingest dimension tables

✅Affordable pricing (seat or insight based)

❌Montly tracked user or events based pricing

✅Easy integration to your warehouse (5 minutes)

❌Extra tool is often needed for ReverseETL

❌Not suitable for small startups without a data warehouse

✅Easy to get started with small startups

❌Performance depends on the data warehouse usage and cluster size.

✅Relatively consistent performance

Last updated 3 months ago