01Module 2 - Data InfrastructureA complete map of the Data Infrastructure module covering data lakes, Spark, Kafka, feature stores, data quality, Delta Lake, and lakehouse architecture for ML.02Data Lake and Data Warehouse for MLThe evolution from database to data lake to lakehouse - when to use each storage architecture for ML training data, feature engineering, and model serving.03Batch Processing with Spark for ML PipelinesHow Apache Spark processes terabyte-scale training data - architecture, DataFrames, partitioning, joins, and integration with Delta Lake for ML feature engineering.04Stream Processing with Kafka for Real-Time MLHow Apache Kafka and Flink enable real-time ML features - topics, consumer groups, exactly-once semantics, streaming feature computation, and architecture patterns.05Feature Store ArchitectureHow feature stores solve training-serving skew with a dual-store architecture - offline store for training, online store for serving, and point-in-time correct retrieval.06Data Quality and Validation for MLWhy data quality is the number-one cause of ML failures in production - Great Expectations, data contracts, PSI distribution monitoring, and pipeline quality gates.07Data Versioning with Delta LakeACID transactions, time travel, schema evolution, and training data versioning with Delta Lake - building reproducible ML pipelines on object storage.08Lakehouse Architecture for MLLakehouse architecture for ML systems - Delta Lake, Apache Iceberg, Apache Hudi, medallion architecture, query engines, and ML pipelines on the lakehouse.
