Today, Snowplow announces the general availability of automatically generated data models in Snowplow Console. This new capability eliminates the manual SQL work that traditionally sits between event tracking and data analysis, enabling teams to generate optimized, analysis-ready tables directly from their data products.

The Challenge: Bridging the Gap Between Events and Analysis

Analytics engineers know the pain well: you've instrumented clean, structured event tracking with rich context entities, but before anyone can analyze that data, someone needs to write complex SQL to filter, join, and flatten it all into usable tables. This manual transformation work creates bottlenecks, slows down insights, and requires coordination between tracking designers and data teams.

The Solution: Self-Service Model Generation

With autogenerated data models, we're removing this friction entirely. Now, any data product in your Snowplow Console can be transformed into analysis-ready tables through a guided, no-code workflow. Simply select which event specifications, entities, and properties you want to include, and your Snowplow Console generates optimized models ready to deploy to your warehouse.

Key Capabilities

• Self-Service Model Generation: A new ‘Data Models’ tab in every data product provides an intuitive interface for configuring and generating models. The guided workflow walks you through selecting your events, entities, and deployment options, producing production-ready code in minutes.

• Flexible Deployment Options: Choose the approach that fits your technical environment and use case. Generate simple SQL views for immediate data access, standalone incremental dbt models for custom implementations, or unified models that integrate seamlessly with Snowplow's existing dbt packages for Unified Digital and Normalize.
• ‍Automatic Data Flattening: Say goodbye to nested JSON structures. Generated models automatically expand your event and entity data into individual columns, creating wide tables optimized for BI tools, reverse ETL platforms, and direct SQL analysis. Single entities flatten into columns, while array entities are preserved for flexible unnesting later.
• ‍Intelligent Event Filtering: For teams using Snowtype for tracking validation, models can filter by event specification ID to ensure only high-quality, validated data flows through. Teams not using Snowtype still benefit from intelligent filtering logic based on event schemas, entities, and cardinalities to access complete historical data without requiring tracking changes.‍
• Multi-Warehouse Support: Generate models for Snowflake and BigQuery warehouses directly from Console, with Databricks support coming soon.

Accelerate time-to-analysis in minutes instead of days!

Why This Matters

• ‍Accelerate Time-to-Value: The journey from "we need to track this" to "here's the analysis" now takes minutes instead of days! Data products become immediately queryable without coordination overhead or custom development work.‍
• Reduce Technical Debt: Stop accumulating one-off SQL scripts and undocumented transformation logic. Generated models follow best practices for incremental processing, performance optimization, and maintainability.‍
• Democratize Data Access: Product managers and analysts can generate the tables they need without waiting for engineering resources. Analytics engineers can focus on complex modeling challenges rather than repetitive flattening work.

Get Started Today

Automatically generated data models are available now for all Snowplow CDI customers using BigQuery or Snowflake loaders. See our documentation for detailed steps to get started.  

If you are new to Snowplow and want to learn more, reach out to our team today!