Data warehouse vs Lake vs Lakehouse architecture

In recent years, the concept of a data lakehouse has emerged as a hybrid architecture that combines the strengths of both data warehouses and data lakes, offering a solution capable of handling the growing demands of data ingestion and analysis. This architecture allows businesses to ingest vast amounts of data through streaming or batch processing and make it accessible to a wide range of users swiftly. Unlike traditional data warehouses, lakehouses facilitate the separation of compute and storage, enabling scalability and efficiency, while addressing the limitations of data lakes by supporting data manipulation, enhanced query performance, and maintaining data integrity. The lakehouse model is increasingly seen as a viable alternative to traditional architectures, with advancements in technologies such as Delta Lake and Apache Iceberg allowing for transactional data processing, thus bringing the vision of a fully functional, integrated data platform closer to reality. Companies like Starburst are leveraging open-source SQL engines like Trino to enhance lakehouse capabilities, making it a promising solution for future analytical needs.