SQLGlot for Open Data Infrastructure Migration

A migrated query that runs is not the same as a migrated query that means the same thing. SQLGlot helps with the first part and can support the second. It does not replace the second.

The practical problem

Warehouse migrations often look like storage migrations until the SQL shows up. Then teams discover dialect-specific functions, implicit casts, date behavior, quoting rules, null semantics, and optimizer assumptions buried in thousands of models.

SQLGlot is useful because it parses, analyzes, and transpiles SQL across dialects. In ODI migration work, that can turn an impossible manual review into an inspectable pipeline. But syntax translation is only one layer of migration risk.

The ODI boundary

Open data infrastructure migration is about keeping the contract intact while tools change. SQL is one contract. Table format is another. Catalog identity, policy, lineage, and business definitions are others.

SQLGlot can help parse the SQL contract and expose what needs attention. It can identify dialect features, normalize expressions, support lineage extraction, and generate candidate SQL for another engine. The ODI work is deciding whether the translated query still produces the same business answer.

Patterns that work

Build a translation inventory before you move workloads. Parse every model and query you care about, then group findings by dialect feature, risk, and owner. Do not start with the hardest dashboard in production. Start with a map.

Use golden-result tests for important models. Run the old and new query against controlled inputs and compare outputs. For time, currency, identity, and slowly changing dimensions, include ugly cases on purpose.

Connect translation to contracts. dbt model contracts, SQLMesh state, and table metadata can help define the expected shape of a result. SQLGlot can help rewrite SQL. The contract tells you whether the rewrite is acceptable.

Failure modes

The first failure is syntactic confidence. A query transpiles, tests pass on a small sample, and a month later finance finds a date boundary error. That is not a tooling failure. That is missing semantic test coverage.

The second failure is hidden dependency drift. Queries depend on warehouse-specific functions, session settings, collations, or implicit casting. The migration plan treats them as SQL text and misses the execution environment.

The third failure is no rollback path. If the translated model becomes the only model before validation is complete, every defect becomes a production defect.

Questions to ask

• Which dialect features appear most often in the migration inventory?
• Which models have golden-result tests before translation?
• Which differences are accepted, rejected, or manually reviewed?
• Can lineage show which reports and agents depend on each translated model?
• Can the team run old and new paths side by side until confidence is earned?

For related migration strategy, read SQLGlot and Portable SQL, How to Migrate Data Platforms Without Downtime, and Migrating the Semantic Layer.

Sources to start with

Start with the primary docs. They are the contracts you can test against, not commentary about the contracts.

• SQLGlot documentation
• SQLGlot GitHub repository
• SQLMesh documentation
• dbt model contracts documentation
• Apache Iceberg table specification