A data architecture that combines the low-cost, flexible storage of a data lake with the structure and query performance of a data warehouse.
Traditionally, organizations stored raw data in a data lake and copied cleaned, structured data into a separate warehouse for reporting. A lakehouse keeps data in open file formats on inexpensive object storage, then adds a table layer that supports reliable updates, schemas, and fast SQL queries. Open table formats such as Delta Lake, Apache Iceberg, and Apache Hudi make this possible.
For example, a streaming service could store viewing logs, subscription records, and support transcripts in one lakehouse, letting analysts run reports while data scientists train recommendation models on the same data. The approach reduces duplicated copies and pipelines. It still requires solid data governance, and teams must choose and manage the table format and query engines carefully.
A central repository that stores large volumes of raw data in its original format until it is needed for analysis.
Data WarehouseA system that stores cleaned, structured data from many sources, optimized for reporting and business analysis.
A plain-English guide to big data: what the 3 Vs mean, real examples, how big data is processed and a simple test for whether your data is actually big enough to matter.
A practical guide to data pipelines: the stages from ingestion to delivery, batch vs streaming, a real business example and the failure-handling habits that keep dashboards trustworthy.
A side-by-side comparison of data lakes and data warehouses covering structure, users, cost and performance, plus when a lakehouse makes sense and how to choose.