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Most teams that move to Databricks get the hard part right. They migrate the processing engine, rebuild the transformation logic, and stand up Unity Catalog. Then they leave Azure Data Factory running in the background: connected to everything, owned by nobody, and quietly accumulating cost and complexity. In this entry, that’s the gap we address.
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ML & AI
Databricks Lakewatch: The Future of Agentic SIEM
Databricks Lakewatch replaces the traditional SIEM model with an Open Security Lakehouse — storing 100% of telemetry in open formats at up to 80% lower TCO. AI agents reason across years of unified data to detect and respond at machine speed, closing the visibility gap that legacy SIEMs were structurally forced to create. Early customers include Adobe and Dropbox, with broader availability following Private Preview.
SQL: Why Materialized Views Are Your Simplest Data Transformation Tool
Create cost-effective, incremental materialized views in Databricks SQL Warehouse. Includes monitoring tips, best practices, and Enzyme optimization.
New Databricks INSERT Features: INSERT REPLACE ON and INSERT REPLACE USING
Databricks SQL introduces two powerful new INSERT commands: INSERT REPLACE ON for conditional record replacement and INSERT REPLACE USING for complete partition overwrites. These Delta-native features eliminate complex workarounds while maintaining data integrity. Available in Databricks Runtime 16.3+ and 17.1+ respectively, these commands provide developers with precise control over data updates and partition management in modern data engineering workflows.
Managed Iceberg Tables
Learn when to choose Apache Iceberg over Delta tables in Databricks. Complete guide covering manifest files, CDC limitations, liquid partitioning, and table properties with practical examples.
Moving from IBM DB2 & DataStage to Databricks (Pt. 1)
Learn how to migrate from IBM DB2 & DataStage to Databricks. A comprehensive guide covering architecture mapping, migration strategies, and best practices for modernization.