Azure Databricks for MLOps and Feature Engineering at Scale with Apache Spark, Delta Lake, and MLflow

Short summary

Tutorial on building production-grade feature engineering pipelines on Azure Databricks using Apache Spark, Delta Lake, and MLflow with Medallion Architecture (Bronze/Silver/Gold layers). Example demonstrates customer churn prediction system with complete PySpark code for each layer, emphasizing append-only Bronze for replay capability and idempotent Silver upserts for data quality. All layers include Delta Lake ACID guarantees and MLflow tracking for reproducibility.

• •Medallion Architecture guides data from Bronze (raw) → Silver (clean) → Gold (features) with MLflow tracking across layers
• •Append-only Bronze layer enables pipeline replay without re-ingestion; Silver uses idempotent merges for data consistency
• •Complete PySpark code examples for aggregations, window functions, and feature encoding with Delta Lake ACID guarantees

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