Data Lake to Lakehouse Migration Checklist
A readiness checklist for evolving a raw data lake into a governed lakehouse using open table formats like Delta Lake or Iceberg. It covers table-format choice, medallion layering, schema enforcement, cataloging, and cost. Use it to turn an ungoverned lake into a reliable analytics base.
When to Use This Checklist
Use this checklist when evolving a raw data lake into a lakehouse. A data lake stores files cheaply but lacks schema enforcement, transactions, and governance. A lakehouse adds open table formats such as Delta Lake, Apache Iceberg, or Hudi to bring ACID transactions, schema evolution, and catalog-driven governance to the same low-cost storage. This migration upgrades a swamp-prone lake into a reliable analytics foundation.
How to Use This Checklist
Begin by inventorying what the lake actually contains, including formats and partition layouts that accumulate over time. Choose a table format and design a medallion layout that separates raw, cleaned, and curated data. Schema enforcement is the defining upgrade, so define enforcement and evolution rules per table. Treat catalog, access control, and cost modeling as required, because ungoverned or unbudgeted lakehouses recreate the problems they were meant to solve.
What Good Looks Like
A successful lakehouse has its data in an open table format with ACID guarantees and enforced schemas, organized into clear bronze, silver, and gold layers. A catalog and access controls govern every table, and quality and freshness checks protect curated layers. Lineage spans the layers, files are compacted and sized for fast queries, and cost is modeled before scale. Analytics and ML workloads run against governed tables with confidence, ending the era of untrusted raw files.
Common Pitfalls
The classic failure is treating the lakehouse as just a new storage location while keeping the old swamp habits, so schema enforcement and governance never take hold. Teams pick a table format without planning compaction, leaving tiny files that cripple query performance. Skipping the catalog leaves data undiscoverable. Cost surprises arise when uncontrolled scans hit cloud storage. Finally, migrating jobs without quality checks lets bad data flow straight into curated tables.
Related Resources
Design around data-lakehouse-architecture and medallion-architecture, and govern with a data-governance-framework. Protect curated data with data-quality-management and trace it with data-lineage.