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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
The Lakehouse Finally Has Real Transactions
Learn how Databricks multi-statement transactions use Unity Catalog catalog-managed commits to guarantee atomic updates across multiple Delta tables — with a step-by-step walkthrough.
Why Your Databricks Upgrade Is Incomplete If You're Still Running ADF
Still running ADF after moving to Databricks? Here's why it happens, what it's costing your governance story, and how Lakeflow Jobs closes the gap.
Your Databricks Stack Is Modern. Your Orchestration Isn't.
Most Databricks migrations modernize the processing engine and leave Azure Data Factory running untouched. This post explains why that gap is a compounding business risk, and maps out the three practical paths to migrating orchestration to Lakeflow Jobs, including the on-prem push pattern that removes the most common blocker.
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.
Deploy Your Databricks Dashboards to Production
Stop deploying Databricks dashboards manually. Learn how to use Git, Asset Bundles, and CI/CD for reliable, reproducible dashboard deployments across environments.
The Nightmare of Initial Load (And How to Tame It)
Initial data loads don't have to be nightmares. Discover the split Bronze table pattern that separates historical backfills from incremental streaming.
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.
Temp Tables Are Here, and They're Going to Change How You Use SQL
Learn how temporary tables in Databricks SQL warehouses enable materialized data, DML operations, and session-scoped ETL workflows. Complete with practical examples.
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.
Hidden Magic Commands in Databricks Notebooks
Discover 12 powerful Databricks notebook magic commands beyond %sql and %python. Learn shortcuts for file operations, performance testing, and debugging.
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.
Excel never dies (and neither does SharePoint)
Learn how Databricks' native Excel import handles multi-sheet workbooks, streaming autoloader, and SharePoint integration. Includes code examples.
Databricks Workflow Backfill
Use Databricks Workflow backfill jobs to reprocess historical data, recover from outages, and handle late-arriving data efficiently.
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.