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Post: Lakehouse Federation - Databricks


Lakehouse Federation - Databricks

🔗 In the world of data, innovation is constant. And the most recent revolution comes with Lakehouse Federation, a fusion between data lakes and data warehouses, taking data manipulation to a new level. This advancement promises deeper insights, improved integration and more assertive decisions, driving business value to unprecedented heights.

🔍 What is Lakehouse Federation? 🤔
Lakehouse Federation, a feature of Databricks, redefines the way we interact with data. It allows you to discover, query and control data from multiple sources, such as MySQL, PostgreSQL, Redshift, Snowflake, Azure SQL, among others, without the need to move or copy data. 🌐

🔐 With Unity Catalog, Lakehouse Federation offers a unified metadata management platform, providing a holistic view of all your data, regardless of where it is stored. This not only facilitates data discovery and control, but also ensures consistent and secure data governance.

📊 Why adopt Lakehouse Federation?
The ability to analyze data from multiple external sources without the need for centralized migration reduces redundancy and data isolation. Whether for ad hoc reporting, proofs of concept, or supporting workloads during incremental migrations, Lakehouse Federation offers an efficient and agile solution.

🔧 Hands-on: Exploring the Lakehouse Federation
In this hands-on, we will explore configuring Lakehouse Federation, accessing data from Azure SQL Database and Azure Database for MySQL. We will dive into configurations, connections, and demonstrate the functionality that Databricks brings to Lakehouse Federation.

📹 Check out the full demo on YouTube:

📹 Also check out the post with a summary about the lakehouse federation:

#databricks #databrickssql #pyspark #lakehouse #lakehousefederation #spark #sparksql #azurecloud #azuredatabricks

Thomaz Antonio Rossito Neto
Data Technical Lead / Data Engineer-Architect