Data Lakehouse
A modern data architecture that combines the advantages of data lakes and data warehouses. It supports open table formats, unified metadata management, and multi-engine access, providing both low-cost storage and high-performance analytics capabilities.
Core Facts
Timeline (last 90 days)
More and more organizations are migrating their data warehouse workloads away from Oracle and toward data lakehouse architectures represented by Databricks.
The Lakehouse architecture aims to combine the low cost and high flexibility of a data lake with the structured query capabilities of a data warehouse.
The data lakehouse architecture achieves ACID transactions, time travel, and schema evolution by introducing open table formats such as Delta Lake and Apache Iceberg on top of object storage.
In a lakehouse architecture, data written must go through processes such as commit and snapshot, and there may be latency between when data is written and when it can be queried, affecting read-after-write scenarios.
All Facts (4)
The Lakehouse architecture aims to combine the low cost and high flexibility of a data lake with the structured query capabilities of a data warehouse.
75%UnverifiedMore and more organizations are migrating their data warehouse workloads away from Oracle and toward data lakehouse architectures represented by Databricks.
50%UnverifiedThe data lakehouse architecture achieves ACID transactions, time travel, and schema evolution by introducing open table formats such as Delta Lake and Apache Iceberg on top of object storage.
50%UnverifiedIn a lakehouse architecture, data written must go through processes such as commit and snapshot, and there may be latency between when data is written and when it can be queried, affecting read-after-write scenarios.
50%