One standard for database change across your whole data estate

Your analytics platforms are core infrastructure. Your governance should treat them that way

The numbers a board sees, the forecasts a business plans around, and the AI models it's investing in all sit on top of schemas: the tables, views and structures that define what the data means. On most analytics platforms, those schemas change without governance. Objects are edited by hand, deployed through scripts nobody versions, and may differ between environments until something breaks.

The consequences surface downstream, at the worst possible moment. Rename a column or change a data type and every report, model and agent built on it can start producing wrong or missing numbers without visibly failing. Only 23% of organizations use formal data governance frameworks, and data quality problems are typically caught at the point of use rather than the point of change.

AI raises the stakes in both directions

AI workloads consume the data, so an ungoverned schema change quietly degrades AI outputs. And AI increasingly generates the change too: every new use case pulls in new data sources and types, every model iteration reshapes tables and views, and AI now writes many of those changes itself. AI usage in database management nearly tripled in the last year, from 15% to 44%.

The answer isn't to slow schema change down. It's to make fast schema change safe. When every change is versioned, tested, deployable and reversible, teams stop hesitating, releases stop breaking, and the data feeding AI stays trustworthy. Governance isn't a constraint on AI adoption; it's what makes faster adoption sustainable.

One workflow, every platform

Flyway Enterprise brings the same advanced capabilities (schema comparison, drift detection, generated deployment and rollback scripts, policy checks and a full audit trail) to operational and analytics platforms alike. Databricks and Snowflake support is available now in public preview, and more platforms follow on the roadmap. One process, one set of skills, one audit trail, instead of a governed transactional estate sitting next to an ungoverned analytics one.

Problems caught at the point of change, not the point of use

Drift detection and change reports catch out-of-process changes before they surface as a broken dashboard or a misbehaving model, and generated rollback scripts mean a bad change can be reversed cleanly rather than fixed forward under pressure.

Control you can prove

61% of organizations have been through a compliance audit, rising to 81% in financial services. With object-level version history, policy checks and deployment records across the whole estate, questions about what changed, who changed it and whether it was tested get answered from the record, on the analytics estate as on everything else.