Episode Details

What’s up everyone, today we have the pleasure of sitting down with István Mészáros, Founder and CEO of Mitzu.io.
 

• (00:00) - Intro
• (01:00) - In This Episode
• (03:39) - How Warehouse Native Analytics Works
• (06:54) - BI vs Analytics vs Measurement vs Attribution
• (09:26) - Merging Web and Product Analytics With a Zero-Copy Architecture
• (14:53) - Feature or New Category? What Warehouse Native Really Means For Marketers
• (23:23) - How Decoupling Storage and Compute Lowers Analytics Costs
• (29:11) - How Composable CDPs Work with Lean Data Teams
• (34:32) - How Seat-Based Pricing Works in Warehouse Native Analytics
• (40:00) - What a Data Warehouse Does That Your CRM Never Will
• (42:12) - How AI-Assisted SQL Generation Works Without Breaking Trust
• (50:55) - How Warehouse Native Analytics Works
• (52:58) - How To Navigate Founder Burnout While Raising Kids

Summary: István built a warehouse-native analytics layer that lets teams define metrics once, query them directly, and skip the messy syncs across five tools trying to guess what “active user” means. Instead of fighting over numbers, teams walk through SQL together, clean up logic, and move faster. One customer dropped their bill from $500K to $1K just by switching to seat-based pricing. István shares how AI helps, but only if you still understand the data underneath. This conversation shows what happens when marketing, product, and data finally work off the same source without second-guessing every report.

About István

Istvan is the Founder and CEO of Mitzu.io, a warehouse-native product analytics platform built for modern data stacks like Snowflake, Databricks, BigQuery, Redshift, Athena, Postgres, Clickhouse, and Trino. Before launching Mitzu.io in 2023, he spent over a decade leading high-scale data engineering efforts at companies like Shapr3D and Skyscanner.

At Shapr3D, he defined the long-term data strategy and built self-serve analytics infrastructure. At Skyscanner, he progressed from building backend systems serving millions of users to leading data engineering and analytics teams. Earlier in his career, he developed real-time diagnostic and control systems for the Large Hadron Collider at CERN.

How Warehouse Native Analytics Works

Marketing tools like Mixpanel, Amplitude, and GA4 create their own versions of your customer. Each one captures data slightly differently, labels users in its own format, and forces you to guess how their identity stitching works. The warehouse-native model removes this overhead by putting all customer data into a central location before anything else happens. That means your data warehouse becomes the only source of truth, not just another system to reconcile.

István explained the difference in blunt terms. “The data you’re using is owned by you,” he said. That includes behavioral events, transactional logs, support tickets, email interactions, and product usage data. When everything lands in one place first (BigQuery, Redshift, Snowflake, Databricks) you get to define the logic. No more retrofitting vendor tools to work with messy exports or waiting for their UI to catch up with your question.

In smaller teams, especially B2C startups, the benefits hit early. Without a shared warehouse, you get five tools trying to guess what an active user means. With a warehouse-native setup, you define that metric once and reuse it everywhere. You can query it in SQL, schedule your campaigns off it, and sync it with downstream tools like Customer.io or Braze. That way you can work faster, align across functions, and stop arguing about whose numbers are right.

“You do most of the work in the warehouse for all the things you want to do in marketing,” István said. “That includes measurement, attribution, segmentati