The architectural significance here is not just faster migration tooling, but a translation layer that can sit between legacy workloads and a new cloud data platform. For teams running Teradata-era applications, that changes the migration pattern from large code-rewrite projects to a phased compatibility strategy. The practical benefit is reduced dependency on a full application refactor before moving workloads, but the trade-off is that translation logic becomes part of the critical path and must be validated like any other production integration layer.
That has direct implications for enterprise rollout planning. Real-time query and workload translation can compress timelines, yet it also adds another component to observe, test, and govern across environments. Architects will still need to account for workload profiling, semantic edge cases, and parity checks between source and target systems. In other words, migration speed improves only if the organization invests in automated testing, governance, and operational readiness around the translated execution path.
For platform and operations teams, the key question is maintainability after cutover. A migration accelerator can lower initial effort, but long-term value depends on how cleanly the translated workloads align with Snowflake-native patterns over time. Teams should treat the solution as a bridge: useful for reducing disruption and preserving continuity, while still requiring a roadmap for code modernization, cost control, and eventual simplification of the data estate.
Snowflake continuously innovates to help our customers modernize on the Snowflake AI Data Cloud, enabling fast migrations while decreasing delays, disruptions and long planning cycles. Our free solutions, SnowConvert AI and Snowpark Migration Accelerator, reduce migration risks and costs and simplify code conversion to help customers realize more value from their data from day one.
We’re thrilled to announce Snowflake has entered into a definitive agreement to acquire the technology powering the Datometry software migration solution, which makes moving from legacy data warehouses like Teradata to Snowflake up to four times faster while reducing costs by 90%. This exciting new venture will enable our current and future customers to accelerate their data journey with even faster migrations using a proven, trusted solution.
Datometry’s solution translates queries, scripts and workloads in real time, allowing applications built for legacy systems to run on Snowflake with minimal code changes.
We’re eager to start integrating the power of Datometry into SnowConvert AI. In addition to bringing Datometry’s capabilities to Snowflake, we’re thrilled to also welcome many talented Datometry employees, including Datometry founder and CEO Mike Waas, CTO Michael Duller, and VP of Customer Success Rima Mutreja. With their deep knowledge and expertise, we’re looking forward to collaborating to bring innovative developments in data migration to the Snowflake AI Data Cloud.
Snowflake already provides our customers with an enterprise-ready cloud-native architecture, AI solutions and support from our partner ecosystem to streamline migrations and help our customers stay focused on innovation. Adding the capabilities of Datometry will enhance our customers’ experience, accelerating migrations by significantly reducing the time and effort required for client applications and tooling migration. This translates directly to a faster time to value.
https://www.snowflake.com/content/snowflake-site/global/en/blog/accelerate-data-migration-datometry-technology
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