Data warehouse migration solutions

Summary

• A migration solution has to do four jobs, not one: assess the source system, convert its SQL and ETL code, move the data, and reconcile the result so nothing is lost. Databricks runs all four through Lakebridge, a free, open migration toolkit.
• The target is the lakehouse: data lands in open Delta Lake tables, is governed by Unity Catalog, and is served to BI through Databricks SQL.
• Databricks favors an iterative migration over a big-bang cutover: inventory and strategy first, then move workload by workload while the old system keeps running, and retire it once the new patterns are proven.
• Lakebridge's assessment step profiles the existing environment (usage patterns, code complexity, dependencies, and lineage) so teams can size the effort and sequence the work before converting a single query.
• An agentic code converter (in Beta) uses the Genie Code agent to translate proprietary SQL dialects into open ANSI SQL, and a reconciliation step compares source and target to prove the move before cutover.

Data warehouse migration solutions

A real migration solution handles the whole path, not just a code translator. It has to assess what you run today, convert proprietary SQL and ETL into a portable form, move the data, and prove the target matches the source before you cut over. Databricks packages those steps into Lakebridge, a free and open toolkit that migrates legacy and cloud data warehouses to the lakehouse, with Delta Lake as the open storage layer, Unity Catalog for governance, and Databricks SQL for analytics. The guiding idea is to modernize while you migrate rather than lift years of technical debt one-for-one into a new system.

What a data warehouse migration involves

Most migrations off a traditional or cloud data warehouse move through four stages:

• Assessment. Inventory the source: tables, stored procedures, ETL jobs, usage patterns, and dependencies, then estimate effort and decide what moves first.
• Code conversion. Translate proprietary SQL dialects, stored procedures, and ETL logic into open ANSI SQL, Databricks SQL, and Spark.
• Data migration. Move historical and ongoing data, often using federation for read access and change data capture for continuous sync.
• Reconciliation. Compare source and target on schema, row counts, and aggregates so you can trust the results before decommissioning the old system.

Treating reconciliation as a first-class stage, not an afterthought, is what separates a controlled migration from a risky one.

What to look for in a migration solution

Capability	Why it matters
Automated assessment	An inventory of code, usage, and dependencies turns guesswork into a scoped, sequenced plan
Automated code conversion	Translating dialects and stored procedures by hand is slow and error-prone; automation removes most of the mechanical work
Data movement and CDC	You need to load history and keep changes in sync while both systems run in parallel
Built-in reconciliation	Schema, row-count, and aggregate checks prove the target is correct before cutover
Open target format	Landing data in an open table format keeps it portable instead of trapped in another proprietary store
Unified governance	One permission, lineage, and audit model across the migrated data avoids rebuilding security per tool
Iterative migration path	Moving workload by workload lowers risk versus a single all-at-once cutover

How Databricks approaches data warehouse migration

Databricks runs migrations through Lakebridge and lands them on the open lakehouse. See the migration overview and replacing a data warehouse with a lakehouse.

• One guided toolkit. Lakebridge brings assessment, code conversion, data migration, and reconciliation into a single end-to-end workflow, with a desktop app to walk teams through each phase.
• Assessment first. The profiler and analyzer read the source environment and produce an inventory with code-complexity scores, interdependencies, and table lineage, so teams can size effort and order the work. See more predictable migrations.
• AI-assisted conversion. An agentic code converter, in Beta, uses the Genie Code agent to translate proprietary dialects into open ANSI SQL, launching parallel agents that validate both syntax and intent. A migration project in the workspace tracks progress and visualizes which objects need to move together.
• Data movement. Lakehouse Federation gives read access to the source without copying it, and Lakeflow Connect handles change data capture and ingestion, with Lakeflow Pipelines for the transformation logic.
• Validated cutover. The reconciler compares source and target on schema, row counts, and aggregates and reports the differences, so cutover is a decision backed by evidence.
• An open target. Data lands as Delta Lake tables in your own cloud object storage, governed by Unity Catalog with its three-level catalog.schema.table namespace, and served to BI by Databricks SQL. Open formats keep the data readable by many engines.

FAQs

What does Lakebridge do?

Lakebridge is a free, open toolkit that covers the full migration: it assesses the source system, converts SQL and ETL code, moves the data, and reconciles source against target. It brings those steps into one guided workflow with a desktop app.

Do I have to migrate everything at once?

No. Databricks recommends an iterative migration: inventory and strategy first, then move workload by workload while the legacy system keeps serving, and decommission it only after the new patterns are validated.

What changes when I move to the lakehouse?

Expect some architectural shifts. Databricks uses a three-level catalog.schema.table namespace, runs each statement as its own transaction rather than database-level transactions, and treats primary and foreign keys as informational rather than enforced. Planning for these early avoids surprises.

Can stored procedures be converted automatically?

Much of the mechanical conversion is automated, through a deterministic transpiler and the Beta agentic converter powered by Genie Code. Complex or unusual logic still benefits from engineer review, which is why the assessment step scores complexity up front.