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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
Why Your Databricks Upgrade Is Incomplete If You're Still Running ADF
Still running ADF after moving to Databricks? Here's why it happens, what it's costing your governance story, and how Lakeflow Jobs closes the gap.
Your Databricks Stack Is Modern. Your Orchestration Isn't.
Most Databricks migrations modernize the processing engine and leave Azure Data Factory running untouched. This post explains why that gap is a compounding business risk, and maps out the three practical paths to migrating orchestration to Lakeflow Jobs, including the on-prem push pattern that removes the most common blocker.
You Pay for the Complexity of Your Move From On-Prem to Cloud
Moving data from on-prem to cloud shouldn't require 5+ systems. Discover why complexity costs you money and how Zerobus Ingest simplifies data pipelines.
Migrate Tables: From External to Managed Tables
Migrate external tables to managed tables in Databricks Unity Catalog using ALTER TABLE SET MANAGED while maintaining business continuity.
PostgreSQL to Databricks Migration: A Simpler Path to the Lakehouse
Discover a complete guide to PostgreSQL to Databricks migration, including analysis, design, development, and deployment phases. Learn how to transform stored procedures, manage data transfer, and leverage the new SQL Scripting feature for a simplified lakehouse journey.
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.