Building an A/B testing analysis framework for mobile gaming on Databricks

A/B testing frameworks on lakehouse platforms like Databricks represent a pragmatic shift in how we architect experimentation pipelines. Rather than maintaining separate systems for event ingestion, feature engineering, and statistical analysis, teams can now consolidate these workflows within a unified governance layer. This matters operationally because it reduces data movement, latency in test results, and the cognitive overhead of managing multiple APIs. I've seen teams lose weeks to consistency issues when experimentation data lives in one warehouse and feature stores elsewhere. What's particularly valuable here is that Databricks enables analysts to write statistical tests in SQL or Python directly against raw event data without ETL friction. The lakehouse approach also democratizes experiment design—your analytics engineers can iterate on test configurations without waiting for data engineering to build specialized pipelines. The trade-off is that you need to be intentional about data quality gates and governance, since direct SQL access to production event streams requires stronger schema management. My recommendation: if you're running multiple concurrent experiments, invest in a standardized metric computation layer within your lakehouse rather than treating each test as a one-off analysis. This compounds across experiments and builds institutional knowledge.