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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
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.
5 Reasons You Should Be Using LakeFlow Jobs as Your Default Orchestrator
External orchestrators can account for nearly 30% of Databricks’ job costs. Discover five compelling reasons why LakeFlow Jobs should be your default orchestration layer: from Infrastructure as Code to SQL-driven workflows.
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.