Databricks for the Intelligent Enterprise
A practical guide to building a governed data and AI foundation on Databricks,
Data engineering, analytics, machine learning, and AI agents often run on separate systems. Each one adds another copy of data, another access model, and another operating cost. Reports disagree, AI systems lack trusted context, and new workloads take longer to reach production because controls get rebuilt for every tool. This Databricks migration guide sets out the decisions leaders face before data and AI work moves onto a common foundation.
What a Governed Lakehouse Foundation Changes
Databricks lakehouse architecture brings data engineering, analytics, data science, and AI onto one data and governance layer. The guide covers four parts of that foundation:
- Ingest: Lakeflow handles batch, streaming, and change data across enterprise sources.
- Store: Delta Lake creates reliable tables with transaction support, schema controls, and recovery.
- Govern: Unity Catalog data governance applies access, lineage, quality, and policy in one control model.
- Use: BI, ML, agents, and applications work from the same certified data.
How to Control a Databricks Migration Across Six Phases
Migration is where the plan meets years of technical debt, undocumented rules, and report dependencies. The guide sets out a six-phase model with an exit control at each stage, running from workload assessment through cutover and legacy retirement. It also shows where Kanerika FLIP automates up to 80% of repeatable conversion work on supported patterns, covering sources such as Informatica PowerCentre, Teradata, IBM DataStage, Netezza, Azure Synapse, SSIS, Talend, and Alteryx.
Move Enterprise AI from Pilot to Production
You will also learn what production AI on Databricks asks for: grounding in approved enterprise data, evaluation against defined thresholds, access and spend controls through Unity Catalog, and monitoring for quality, drift, and cost. Two case studies show measured results, including 90% lower data lag in fraud and settlement reporting, and zero production downtime during a retail migration that retired the full legacy estate.
Kanerika has delivered 75+ Databricks implementations with 30+ certified Databricks engineers across 10+ industries.
Build a clear path from Databricks strategy to production with the right foundation, migration approach, and governance model for enterprise-scale data and AI.
Download the Databricks Whitepaper
