October 2025 delivered a major analytics enhancement for mozilla/bigquery-etl by adding an ended_reason field to track why subscriptions ended across Stripe, Google, and Apple platforms. The change introduced a consistent ended_reason schema across all subscription data, enabling reliable churn analysis and richer historical views. This work aligns with DENG-4043 and is implemented through three commits that span Stripe, Google, and Apple logical subscriptions. Overall, there were no major bugs fixed this month; the focus was on feature delivery, data quality, and establishing scalable cross-platform analytics. Business impact includes improved visibility into churn drivers and data-driven retention strategies, supported by a standardized schema and traceable commits.

September 2025 performance summary for mozilla/bigquery-etl: Delivered a major cleanup of the BigEye API data surface by removing obsolete schema.yaml files across services and deprecating unused pipelines, reducing maintenance burden and risk of stale configurations. This work enhances data governance, simplifies future migrations, and frees engineering effort for higher-value initiatives.

August 2025 monthly summary for mozilla/bigquery-etl: Focused on stabilizing deployment by aligning data schemas and fixing data type mismatches to ensure accurate data handling and reliable ETL runs. Key fixes address schema alignment (INTEGER -> FLOAT) and BigEye deployment issues for collection_metric_status.

July 2025: Delivered automated ingestion of BigEye API data into BigQuery across key services (dashboard, collection V2, virtual table, workspace, user, group, issue, metric), enabling daily and monthly analytics. Expanded Looker coverage by enabling BigEye API data integration through YAML-based LookML configurations. Completed production-ready schema alignment fixes to ensure dashboards reflect the production model, correcting the dashboard type field (TIMESTAMP -> DATETIME) and resolving YAML schema issues for collection and metric services. Result: improved data freshness, reliability, and cross-service visibility, empowering faster, data-driven decisions. Key technologies: Python data pipelines, BigQuery, BigEye API, Looker LookML/YAML, schema validation, robust error handling.