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Endigest AI Core Summary
This article describes how HARDlight built a Databricks-native A/B testing analysis framework to automate and standardize mobile game experimentation at scale.
•Experiment data is ingested into governed tables via Spark Declarative Pipelines, with statistical models computed in notebooks and materialized into a unified analytics layer.
•Databricks AI/BI surfaces results through a daily-refresh dashboard starting with an LLM-generated summary for non-technical stakeholders, with progressive disclosure for deeper analysis.
•Unity Catalog manages permissions and lineage of experiment assets, while MLflow handles experiment tracking and model packaging for reproducibility.
•A "frozen dashboard" feature preserves final experiment snapshots, decisions, and recommended actions at the end of each run, creating an auditable archive.
•The framework reduced manual analysis effort by over 8 hours per week and enabled a 2x increase in monthly A/B testing capacity without adding headcount.
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Databricks
Google Cloud