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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
Bridge the Gap in Your Data Stack: Leverage Databricks BI/AI to Enhance Traditional BI
Bridge the gap in your data architecture by strategically combining Databricks BI/AI with traditional business intelligence tools. Learn how to reduce licensing costs, improve dashboard performance, and implement a hybrid approach that leverages the best of both worlds. This practical guide shows you when to use native Databricks capabilities versus tools like Power BI for optimal cost-efficiency and performance.
The Chaos of Data: How Fragmentation is Stalling Innovation
Discover how data fragmentation across multiple platforms creates costly silos and slows decision-making. Learn why Databricks offers the unified solution modern enterprises need.
Seamless Data Integration: SAP to Databricks
Learn how to integrate SAP data into Databricks with this comprehensive blog. Discover the essential components of the SAP ecosystem, including SAP HANA, S/4HANA, and BTP, and explore proven integration methods using SparkJDBC and Azure Data Factory. Perfect for data engineers and architects looking to combine SAP's enterprise management capabilities with Databricks' advanced analytics.