Snowflake Signs Definitive Agreement to Acquire Datometry Assets

By Betsy Burton

Snowflake Secures Definitive Agreement to Acquire Datometry Assets

As we have predicted, we are seeing intense competition among cloud data warehouse providers continues to drive strategic acquisitions. November 10, 2025, Snowflake announced definitive agreement to acquire the technology and team behind Datometry’s software migration solution.

This news marks a concerted effort by Snowflake to eliminate a major barrier for enterprises looking to leave legacy data warehouses. Snowflake officially disclosed that it has entered into a definitive agreement to acquire the assets, signaling a binding commitment to integrate this powerful solution.

The deal, the financial terms of which were not disclosed, signifies Snowflake’s continued investment in easing the transition to its platform. Datometry’s proprietary solution has been cited as a game-changer for large-scale enterprise modernization. This strategic move directly impacts the migration services ecosystem, potentially streamlining the path for large, complex workloads to shift to the Snowflake AI Data Cloud.

Enhanced Real-Time Translation Capabilities

The core of the acquisition is Datometry’s technology, particularly its flagship product, Hyper-Q. This will be an enhancement to SnowConvert AI, as Snowflake plans to integrate the technology directly into its current migration tool.

Hyper-Q acts as an intelligent middleware, a layer of database virtualization that intercepts, translates, and emulates proprietary SQL statements, scripts, and database features (like stored procedures and macros) in real-time. This  approach allows applications built for legacy environments to run on Snowflake with minimal or no changes to the application code or APIs. The technology eliminates the need for developers to rewrite millions of lines of complex SQL, which is the main sticking point and cost driver in traditional migrations.

The significant innovation here is the real-time translation and emulation capability, which enables organizations to simply point their legacy applications to the new Snowflake instance. This cuts the typical migration timeline from years to months and radically reduces the associated costs.

Why was this Announced?

Snowflake’s primary motivation for the Datometry acquisition is clear: accelerate cloud adoption and shorten the time-to-value for new customers. The single biggest hurdle for large enterprises considering a move to the cloud is the perceived risk, time, and exorbitant cost of migrating mission-critical applications tied to legacy systems.

By making migration nearly seamless, Snowflake is better positioned to win large enterprise customers currently locked into legacy platforms, such as those from Teradata or Oracle. The integration into SnowConvert AI solidifies Snowflake’s migration offering as a best-in-class, highly automated solution, which, in turn, facilitates faster customer onboarding and accelerates Snowflake’s usage-based revenue model.

Acquisition Strengths

The acquisition delivers a significant technological leap in migration automation, which provides considerable strength for Snowflake and its customers.

By enabling application independence from the underlying database, Datometry simplifies an otherwise major engineering task. This significantly reduces the business risk associated with modernization projects, ensuring continuity and faster time-to-market for leveraging modern cloud capabilities like the AI Data Cloud.

The claimed reduction in migration cost and time, if delivered, could dramatically shift the economic equation for enterprises, strengthening the business case for moving to Snowflake

Also, this move reinforces Snowflake’s status as the platform built for enterprise scale and migration, strengthening their position against traditional data warehouse incumbents by effectively nullifying their main lock-in mechanism: proprietary SQL and application dependencies.

Acquisition Challenges

One of the biggest challenges of this merging of technologies is ensuring its stability, scalability, and performance parity for all legacy systems and will require substantial internal resources to execute successfully.

A further challenge lies in long-term customer strategy, as real-time translation is a robust solution for initial migration, but some enterprises may still prefer full code conversion for long-term application maintenance and to fully optimize for the target platform. Snowflake must manage customer expectations around whether the translated code will achieve the same performance as natively written SQL.

Finally, this aggressive strategic move will undoubtedly spur a competitive response, forcing rivals to seek counter this advantage through new product development, enhanced partnerships with similar migration tool vendors, or more aggressive pricing and service offerings to slow customer attrition.

The market impact is a signal that migration complexity is now a solvable problem, putting immense pressure on legacy data warehouse vendors.

Bottom Line

The potential impact on Snowflake is overwhelmingly positive: it solidifies their technology moat and directly addresses their largest sales barrier.

The integration of Datometry’s real-time translation technology into SnowConvert AI significantly reduces the cost, time, and risk of moving from legacy data warehouses to the cloud. This strategic purchase is not just about technology; it’s about removing the final business impediment to cloud adoption.