Databricks Expands Lakehouse Capabilities and Formats for AI-driven Data Analytics

Databricks is expanding its approach to data lakes by introducing Universal Format (UniForm), which allows data stored in Delta Lake to be read as if it were Apache Iceberg or Apache Hudi. This move aims to simplify the compatibility issues between Delta, Hudi, and Iceberg, and broaden Databricks' appeal in machine learning and data warehouse-type workloads. Meanwhile, Snowflake has announced updates to its Iceberg Tables, aiming to eliminate data silos and support unstructured data lake-style workloads. The battle between Databricks and Snowflake in the data lake world continues, with both companies striving to offer a unified platform for data management and analytics.
- Databricks puts cards on the table format as Snowflake looks for more players The Register
- How Databricks is adding generative AI capabilities to its Delta Lake lakehouse InfoWorld
- Databricks Puts Unified Data Format on the Table with Delta Lake 3.0 Datanami
- Databricks Unveils Lakehouse AI - A Platform For Building Generative AI Models Forbes
- Databricks builds a data mesh with the launch of Lakehouse Federation TechCrunch
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