Upleveling our SQL models to manage DBT query speeds

At Stytch, the team has enhanced their SQL-based product analytics pipeline to handle increased data and query loads efficiently, using dbt as the transformation layer. Initially, this pipeline was designed to process raw event data into an analytics layer within their Snowflake data warehouse, with a focus on staging, reporting, and data marts to maintain performance and organization. However, as API usage grew, the team faced performance bottlenecks, particularly with queries using count(distinct) aggregations. To address this, they adopted incremental updates and optimized queries by leveraging Snowflake's window functions to reduce data scanning requirements. This strategy significantly reduced query execution times, allowing the team to streamline their whole model hierarchy and improve the pipeline's execution time from 45 minutes to under four minutes, demonstrating the effectiveness of logical data flow refactoring.

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