Why Every Analytics Engineer Needs to Understand Data Architecture

Data architecture provides the essential blueprint for how data is stored, moved, and transformed within an organization, determining overall efficiency. The historical evolution began with relational databases for operations and data warehouses for analytics, with competing design philosophies from Inmon and Kimball. The data lake was introduced to store massive, varied datasets with a flexible schema-on-read approach, but often resulted in unusable "data swamps." To resolve this, the data lakehouse architecture emerged, combining the scale of a data lake with the transactional capabilities of a warehouse using layers like Delta Lake or Apache Iceberg. A more recent paradigm, the data mesh, proposes a decentralized, sociotechnical approach where domain experts own and manage their data as a product.