Blend reduces time-to-value by 4 months using dbt Cloud and Monte Carlo
Blend's data team relied on manually scheduled SQL queries in Airflow that drained engineering resources and made product analytics fully dependent on data engineering for every new workflow request. Insufficient data quality monitoring led to a revenue-skewing incident caused by poor data quality calculation.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Multi-source data intake
Data arrives from 32 sources and is consumed by 12 different teams on top of the platform.
Blend reduced time-to-value by 4 months compared to their internal POC framework, gained automated data quality coverage across all production tables, and reduced warehouse compute costs through metadata-based monitoring and pre-processed SQL operations.
What failed first
Blend's in-house POC data quality framework used orchestrated validation queries that completely overwhelmed the Redshift warehouse with CPU-heavy, slow-running queries, making it unviable as a production solution.