Unity Catalog introduces many new concepts in Databricks, particularly around security and governance. One significantly improved security feature that Unity Catalog enables is Row Level Security (hereby referred to as RLS).

Apache Spark offers tremendous capability, regardless of the implementation—be it Microsoft Fabric or Databricks. However, with vast capabilities comes the risk of using the wrong “tool in the shed” and encountering unnecessary performance issues.

TL;DR For developers, Chocolatey is an essential tool to address the challenges of installing and managing software.

Ever wished you could add dynamic content, parameterize, or reference a Key Vault secret value for Linked Service properties that only accept static inputs in the Azure Data Factory or Azure Synapse UI? In this post, I’ll introduce you to a feature that’s often overlooked, but incredibly handy for these purposes.

Since its introduction, Unity Catalog has been creating significant buzz in the data community. But what exactly is it, and why is enabling it in your Databricks workspace so important? This article dives into the essence of Unity Catalog, demonstrating how it revolutionizes data engineering in lakehouses and provides guidelines for enabling it in your existing Databricks Workspace.