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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
Snowflake and Databricks: How to balance compute
Compare Snowflake and Databricks compute models. Learn scaling strategies, cost optimization tips, and when to use auto-suspend, multi-cluster, and autoscaling.
Purpose for your All-Purpose Cluster
Learn how to configure Databricks all-purpose clusters to reject scheduled jobs, forcing teams to use cost-effective job clusters. Simple setup, big savings.
New Classic Compute Policies: Prevent Overspending
Configure Databricks compute policies to prevent accidental overspending. Step-by-step guide to setting auto-termination, worker limits, and access controls that protect your budget without limiting productivity.