Data Warehouses and Data Lakes: Architecture Essentials
Data Warehouses and Data Lakes: Architecture Essentials Data teams collect information from many sources, from transactional systems to logs and external feeds. A good architecture helps turn that flux into reliable insights. Two patterns guide most setups: data warehouses for curated analytics and data lakes for large, flexible storage. A modern approach, often called a lakehouse, combines both ideas in one place. Core components matter. Ingestion pulls data from source systems, batch feeds, and real-time streams. Storage uses cheap, scalable options for raw data, and a separate, structured area for curated data. Processing transforms data to fit use cases, either near the data (ELT) or before it (ETL). Access tools, like BI dashboards or SQL engines, read the data safely. Governance covers quality, security, and policy, while metadata and a catalog describe what sits in the system. ...