What a data warehouse is and how it differs from an operational database: architecture, columnar storage, partitioning and clustering, and load patterns.
What a data lake is, how schema-on-read works, how lakes differ from warehouses, and how raw, cleaned, and curated zones keep a lake from becoming a swamp.
How CSV, JSON, Avro, ORC, and Parquet differ, why columnar formats suit analytics, and how compression codecs affect file size and query speed.
The dimensions of data quality, where quality breaks down in pipelines, how to validate data at each stage, and tools that catch problems before users do.
