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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
Prioritize AI Quality by Establishing a Data Quality Pillar
AI quality isn't just a model problem — it starts with your data. This guide outlines six executive-grade requirements for establishing a data quality pillar in Databricks, and explains how agentic monitoring can help organizations scale quality across their entire data estate.
Deduplicating Data on the Databricks Lakehouse: Making joins, BI, and AI queries “safe by default.”
Learn 5 proven deduplication strategies for Databricks Lakehouse. Prevent duplicate data from breaking AI queries, BI dashboards, and analytics. Includes code examples.