TL;DR:
- Databricks introduces Lakehouse Federation, enhancing its Unity Catalog.
- Lakehouse Federation enables access, governance, and processing of external data.
- Unity Catalog centralizes data management and governance across platforms.
- Users can set data access policies and execute analytics across various databases.
- Integration with Apache Hive API and upcoming features further enhance capabilities.
- Databricks embraces the data mesh architecture, empowering organizations.
- Unity Catalog supports data governance and AI governance.
- Lakehouse Federation simplifies big data management and processing.
- Databricks strengthens its strategic position in the market.
Main AI News:
In a bid to bolster its position as a leader in the big data industry, Databricks has introduced Lakehouse Federation, a set of advanced capabilities within its Unity Catalog. These new features empower Delta Lake customers to access, govern, and process data residing outside of the traditional lakehouse infrastructure. With Lakehouse Federation, Databricks aims to pave the way for a data mesh architecture, revolutionizing how organizations manage and analyze their data.
With the addition of Lakehouse Federation capabilities to its Unity Catalog, Databricks offers customers the opportunity to centralize data management and governance functions across all data platforms. This centralized approach eliminates the need for users to migrate or duplicate data, providing a seamless experience. Users can effortlessly manage and govern data from the Unity Catalog tool, a free resource offered by Databricks.
Unity Catalog not only enables users to establish and enforce data access policies on various data sources, including Snowflake, Amazon Redshift, Azure SQL Database and Azure Synapse, BigQuery, MySQL, and PostgreSQL, but it also empowers them to execute data analytics and machine learning workloads by combining data from these diverse databases and data warehouses. This unified approach simplifies data operations and promotes efficient collaboration among different teams.
During the recent Databricks Data + AI Summit, Matei Zaharia, the CTO and co-founder of Databricks, highlighted the advantages of the Unity Catalog and Lakehouse Federation. He emphasized the seamless integration of various data sources within Databricks, where they appear as catalogs with comprehensive permission settings and audit logs. Zaharia also emphasized the optimization efforts made to enhance the performance of queries spanning multiple data sources, ensuring users receive exceptional query response times.
Databricks has been actively expanding the capabilities of the Unity Catalog. Recently, it announced support for the Apache Hive API, allowing the catalog to integrate with any product that supports the Hive catalog. Despite the availability of faster engines such as Presto, Trino, and Spark SQL, many organizations continue to rely on Hive for data management purposes. The upcoming previews of Lakehouse Federation feature, including visibility into third-party data sources and query push-down, along with Hive API compatibility, are eagerly anticipated.
Driven by customer demand for a more streamlined big data experience, Databricks is responding with Lakehouse Federation. The proliferation of data silos within organizations has complicated data management and processing tasks, leading to increased costs and complexity. To overcome these challenges, the data mesh architecture has emerged as a potential solution. Databricks, now a proponent of the data mesh concept, positions Unity Catalog and its Lakehouse Federation capabilities as key technologies for organizations to embrace and implement their own data mesh strategy.
Zaharia emphasized the significance of Lakehouse Federation, stating that it empowers users to perform a wide range of data-related tasks across all their data assets, including data science, analytics, machine learning, and generative AI. The unified approach offered by Databricks’ technology facilitates the implementation of a data mesh architecture with distributed ownership, making the data ingestion process and working with the latest data significantly easier.
Unity Catalog was officially unveiled by Databricks at the Data + AI Summit in 2021 and became generally available a year ago at the same event in 2022. The recent enhancements to Unity Catalog, coupled with Databricks’ acquisition of Okera and investment in Immuta, showcase the company’s strong focus on data governance. Alongside data governance, Databricks is also venturing into AI governance, with the introduction of a product called Governance for AI, which aims to automate the management of data scientists’ entities involved in AI development, including unstructured data files, models, features, and functions. By consolidating these objects within the Unity Catalog, Databricks simplifies the management and governance of AI projects.
Conclusion:
Databricks’ introduction of Lakehouse Federation and the continuous development of its Unity Catalog signify a significant step towards enabling the adoption of the data mesh architecture. By providing customers with enhanced capabilities for accessing, governing, and processing data across various platforms, Databricks empowers organizations to streamline their big data operations. The integration of Apache Hive API and forthcoming features further expands the catalog’s functionality. With a focus on data governance and the introduction of AI governance, Databricks is poised to meet the growing demands of the market and solidify its position as a key player in the industry.
