How do you implement data lake architectures and the lakehouse pattern?
Data lakes store raw data in open formats for flexible analytics, and the lakehouse pattern adds reliability features traditionally found in data warehouses. Data lake: store data in open formats (Parquet, ORC, Avro, JSON) on object storage (S3, GCS, ADLS), organized by domain, source, and arrival date. Benefits: schema-on-read flexibility, cost-effective storage, support for all data types (structured, semi-structured, unstructured), and decoupled storage and compute. Lakehouse: adds ACID transactions, schema enforcement, time travel, and performance optimizations to data lakes using table formats. Table formats: Delta Lake (Databricks, most popular, strong Spark integration), Apache Iceberg (vendor-neutral, growing rapidly, excellent metadata management), and Apache Hudi (optimized for incremental processing and streaming). Key features: ACID transactions (safe concurrent reads and writes), time travel (query data as it existed at any point in time, useful for debugging and reproducibility), schema evolution (add columns without rewriting data), partition evolution (change partitioning strategy without rewriting data), and compaction (optimize small files into larger ones for query performance). Implementation: organize storage in zones (raw/bronze: data as-is from sources, refined/silver: cleansed and standardized, curated/gold: business-ready datasets), implement a table format from day one (avoid raw file-based data lakes that become swamps), register tables in a metastore (Hive Metastore, AWS Glue Catalog, Unity Catalog) for discoverability, and implement access control at the table and column level. Query engines: Spark for heavy transformations, Trino/Presto for interactive SQL queries, and DuckDB for local analysis of Parquet files. Cost optimization: implement lifecycle policies (move cold data to cheaper storage tiers), use columnar formats with compression (Parquet with Zstandard compression), and implement partition pruning for efficient queries.