Catch the schema changes that break pipelines. Compare source and warehouse schemas, detect breaking changes before dbt runs fail, and keep your ETL, analytics, and ML workflows healthy.
Data pipelines are only as stable as the schemas they depend on. A small upstream change can cascade into failed dbt tests, broken dashboards, and stale ML features.
A dropped or renamed column in a source database turns a nightly dbt run into a fire drill. SchemaLens catches the change before the pipeline starts.
A VARCHAR widened to TEXT, a TIMESTAMP narrowed, or a precision change can alter aggregations and downstream reports.
When staging, production, and warehouse schemas drift apart, notebooks and pipelines that worked yesterday fail today with no clear cause.
Teams promise stable schemas to downstream consumers, but without automated checks, those contracts rely on manual communication and hope.
A lightweight, no-install layer of schema visibility that plugs into the tools data teams already use.
Compare a production MySQL schema against a PostgreSQL warehouse schema. Spot missing tables, column drift, and incompatible types in seconds.
Automatically flag dropped columns, removed tables, NOT NULL additions, type narrowing, and other changes that break downstream pipelines.
Post markdown diff reports to PRs, fail Check Runs on breaking changes, and archive HTML reports as pipeline artifacts for auditability.
Generate migration SQL, rollback scripts, and structured reports that fit into dbt docs, data catalogs, and incident post-mortems.
Monitor production schemas over time and get notified when drift occurs — before a downstream consumer notices the mismatch.
Commit a schemalens.lock file and verify it in CI. Catch unauthorized or accidental schema drift automatically.
Add SchemaLens checks to the hand-off points where schema changes enter your data platform.
Pull CREATE TABLE and ALTER TABLE DDL from source databases or migration folders. Use the free schema export command generator for PostgreSQL, MySQL, SQL Server, and more.
Diff source schemas against the previous baseline in CI. The SchemaLens GitHub Action, GitLab CI template, or CLI returns a breaking-change report and risk score.
Only promote changes that pass the schema check. Use the migration SQL and rollback output to update warehouse staging layers safely.
Send schema drift alerts to Slack or Teams when production schemas change unexpectedly. Review the drift alert dashboard to track changes over time.
Free tools that fit into the modern data stack — from dbt CI to Airflow validation.
Compare any two SQL schemas and get migration SQL, rollback scripts, and a breaking-change report.
Compare schemas →Map foreign-key and view dependencies to find the safe migration order for warehouse tables.
Analyze dependencies →Score schema complexity and spot risk factors like wide tables, high nullability, and dense foreign keys.
Score schema →Generate a schemalens.lock file from any schema and verify it in CI to catch drift.
Answer four questions and get a ready-to-use workflow for GitHub Actions, GitLab CI, Jenkins, CircleCI, or Bitbucket.
Generate workflow →Track schema changes across environments and get Slack or Teams alerts when drift is detected.
View dashboard →A 32-point checklist for production schema changes — useful for data platform change review boards.
Open checklist →Test the free SchemaLens diff API in your browser and generate client code in Python, JavaScript, Go, and more.
Try API →SchemaLens runs wherever your data pipelines run. No new infrastructure required.
Data engineers use schema diff to compare source database schemas against warehouse schemas, catch column drops or type changes that break dbt models, and validate that ETL pipelines will still work after a deployment.
Yes. SchemaLens supports PostgreSQL, MySQL, SQLite, SQL Server, and Oracle. You can diff schemas even when the source is MySQL and the warehouse is PostgreSQL.
No. SchemaLens is 100% client-side for the web diff and runs as a read-only check in CI/CD. It never executes SQL against your database; it only compares schema definitions and reports risks.
Yes. Use the schema-diff CLI, GitHub Action, or free API in your dbt CI jobs or Airflow DAG validation steps to fail builds when a breaking schema change is detected.
Add a free, automated schema diff check to your data engineering workflow and catch breaking changes before dbt, Airflow, or your warehouse jobs fail.