Case Study: Metacog doubles release cadence and saves 28% on infrastructure with Databricks

Metacog - Customer Case Study

Metacog is a learning-analytics SaaS that uses machine learning to score open-ended performance tasks for education, corporate, and government customers. The team needed to train and deploy many custom models quickly, but running and testing code on real Apache Spark clusters was manual, complex, and unreliable; developers often tested locally, which caused late-stage bugs, release delays, and slowed product iteration.

Metacog partnered with Databricks to automate their Spark test-and-release pipeline using integrated notebooks, APIs, Jobs, and Jenkins/S3 integration so teams could run code on real clusters and deploy automatically. The result: release cadence doubled (12→24 releases/year), ~28% infrastructure savings, new-hire onboarding time cut by 75%, and 20% of engineering time reallocated from maintenance to product development, with faster bug detection and shorter release cycles.

Metacog

Doug Stein

CTO