From Data Warehouse to Lakehouse: A Migration Playbook

The traditional data warehouse served enterprises well for decades. But the demands of AI, real-time analytics, and unstructured data have exposed its limitations. The lakehouse architecture — combining the reliability of a warehouse with the flexibility of a data lake — is the modern answer.

This playbook outlines the approach we use with enterprise clients migrating from legacy data warehouses (SQL Server, Oracle, Teradata) to modern lakehouse architectures on Azure — using Synapse Analytics, Databricks, or Microsoft Fabric.

Phase 1: Assessment

Map your current data estate — sources, pipelines, transformations, consumers. Identify what moves as-is, what gets refactored, and what gets retired. This phase typically takes 2-4 weeks and saves months of rework later.

Phase 2: Architecture

Design the target lakehouse — medallion architecture (bronze/silver/gold), compute strategy, governance model with Purview, and the integration layer that connects to your existing BI tools and downstream consumers.

Phase 3: Migration

Execute in waves — starting with the highest-value, lowest-risk workloads. Run parallel environments during transition. Validate data quality at every stage. Cut over when confidence is high.

Phase 4: Optimization

Once migrated, optimize for cost (right-size compute, implement auto-pause), performance (caching, partitioning), and governance (lineage, access controls, data quality monitoring).