Data Lake and Warehouse Architecture Questions

Designing scalable data platforms for analytical and reporting workloads including data lakes, data warehouses, and lakehouse architectures. Key topics include storage formats and layout including columnar file formats such as Parquet and table formats such as Iceberg and Delta Lake, partitioning and compaction strategies, metadata management and cataloging, schema evolution and transactional guarantees for analytical data, and cost and performance trade offs. Cover ingestion patterns for batch and streaming data including change data capture, data transformation approaches and compute engines for analytical queries, partition pruning and predicate pushdown, query optimization and materialized views, data modeling for analytical workloads, retention and tiering, security and access control, data governance and lineage, and integration with business intelligence and real time analytics. Also discuss operational concerns such as monitoring, vacuuming and compaction jobs, metadata scaling, and strategies for minimizing query latency while controlling storage cost.