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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
Genie Code Analysis: Two Weeks Later
Databricks Genie Code hit a 77.1% task success rate in production data science workflows — more than double what general-purpose coding agents achieve. But that performance is entirely conditional on the quality of your Unity Catalog metadata. SunnyData's two-week evaluation breaks down what works, what doesn't, and the governance layer you need before you go live.
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.
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.
95% of GenAI projects fail. How to become part of the 5%
MIT reports 95% of GenAI investments produce zero returns. Learn the 5 failure modes keeping AI projects stuck in pilot limbo and how to ship production AI.
Keeping Up With Agent Bricks
Complete Databricks Agent Bricks review and tutorial. Explore the potential of this Databricks tool by building an AI agent for financial analysis using RAG, multi-agent systems, and PDF parsing.