Snowflake Acquisition of Datometry Is a Bet on Smarter Data Migration

When cloud data storage vendor Snowflake announced its acquisition of Datometry, a San Francisco-based startup, it aimed to integrate Datometry’s database virtualization platform Hyper-Q into Snowflake’s AI-powered migration tool, with a view to reducing the costs and complexity of migrating legacy data warehouses to Snowflake’s industry-leading cloud platform. 

Data migration has long been a major source of friction for organizations, especially as they attempt to consolidate and prepare their data for new AI workloads. Datometry’s solution targets one of the most challenging aspects of migrations: translating legacy SQL code and database schemas into a new system without requiring extensive manual rework. Hyper-Q is effectively a compatibility layer that allows applications and databases based on legacy technology to run on modern cloud databases with minimal changes.

Snowflake claims that Hyper-Q can speed up migrations from legacy data warehouses by as much as 400 percent, while also reducing costs by up to 90%. While the latest move directly addresses CIOs’ concerns about the cost of cloud transitions, it also gives Snowflake a competitive edge in a market dominated by rivals like AWS RedShift, Google BigQuery, and Azure Synapse. The acquisition is also characteristic of the intensifying ‘cloud data platform wars’, which has seen competitors like Databricks and others investing heavily in AI to ease data migration woes.

Across the board, there’s a growing trend of automating data management, which is fast becoming a critical business enabler in a time when enterprise data sets are exploding in volume and variety, and leaders face increasing pressure to consolidate, secure, and scale. Manual operations like translating code and aligning schemas have long been barriers to that endeavor, hence why many data platform vendors have prioritized the roll-out of AI-powered migration and integration. Not only are these AI-driven tools orders of magnitude faster—they’re also less prone to human error, and they can validate conversions at scale. That doesn’t mean they can eliminate risk entirely, but it can greatly reduce the cost and workforce burdens of these otherwise enormously complex projects.

For software companies, these developments are likely to lead to faster and higher cloud adoption and a more competitive landscape where enterprises can prioritize real business value rather than risk getting locked in to a single vendor.