Snowflake announced on November 10, 2025, that it had entered into a definitive agreement to acquire the technology powering Datometry’s migration solution and bring key Datometry employees into Snowflake. The technology virtualizes legacy workloads, allowing many Teradata applications to run on Snowflake with minimal code changes while teams modernize in stages.
What Snowflake actually agreed to acquire
The wording of the announcement matters. Snowflake said it would acquire the technology powering Datometry’s software migration solution—not that it had disclosed a conventional purchase of every asset, contract, liability or share of Datometry Inc.
Snowflake named Datometry founder and CEO Mike Waas, CTO Michael Duller and VP of Customer Success Rima Mutreja among the employees joining Snowflake. Datometry separately described its team as joining Snowflake. Neither announcement disclosed a purchase price, payment mix, closing date, revenue contribution, customer count or contract-transfer terms.
A precise description is therefore: Snowflake agreed to acquire Datometry’s migration technology and bring key members of the Datometry team into Snowflake. Customer organizations should confirm support arrangements, contract ownership and roadmap commitments for their specific deployments.
Snowflake’s announcement is available at Snowflake’s acquisition announcement, while Datometry published its own account at Datometry joins Snowflake.
Why Teradata migrations are difficult
Moving tables is only one part of replacing a mature enterprise warehouse. A Teradata estate can include years of SQL, stored procedures, BTEQ scripts, MultiLoad and TPump jobs, scheduling rules, business-intelligence connections, operational utilities and undocumented assumptions about data types, transactions, workload management and performance.
A conventional migration may require teams to:
- Translate SQL dialects and procedural code.
- Rebuild ETL, orchestration and operational tooling.
- Modify reporting and other applications.
- Move and synchronize data while both platforms run.
- Compare results, performance and security behavior.
- Plan a production cutover and rollback.
That work can turn a platform replacement into a multiyear rewrite. Datometry addresses the application-compatibility portion of the problem rather than treating migration as a table-copy exercise.
How Datometry’s virtualization approach works
Datometry provides a compatibility layer that translates queries, scripts and workloads in real time so existing applications can operate against Snowflake with minimal code changes. In practical terms, an organization can expose or move data to Snowflake, keep important applications functioning, and convert code incrementally instead of waiting for a complete rewrite before obtaining value.
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- Move or make required data available in Snowflake.
- Use the virtualization layer to keep qualifying legacy workloads operating.
- Validate outputs, security, latency and operational behavior.
- Rewrite high-value workloads into native Snowflake SQL and pipelines in phases.
- Retire Teradata and the compatibility layer only after production confidence is established.
“Minimal code changes” does not mean universal zero-change compatibility. Unsupported syntax, utilities, external dependencies and behavior differences still require remediation and testing.
Virtualization versus rewriting
| Consideration | Datometry/Snowflake AIM virtualization | Code conversion and native modernization |
|---|---|---|
| Primary objective | Run existing workloads on Snowflake with limited initial disruption | Convert code, data and pipelines to Snowflake-native operation |
| Initial rewrite requirement | Lower, but exceptions still need remediation | Higher; generated code requires review and testing |
| Time to first value | Designed for an earlier workload move | Usually later, after conversion and validation |
| Long-term architecture | Transitional compatibility followed by selective modernization | Native Snowflake implementation |
| Main trade-off | Speed and continuity versus continuing translation dependence | Greater effort upfront versus simpler long-term operation |
| Best fit | Large, poorly documented estates, tight renewal deadlines or low tolerance for disruption | Accessible codebases with time for regression testing and redesign |
Virtualization can accelerate an exit from Teradata, but it can also preserve legacy access patterns that an organization eventually wants to remove. A phased plan often uses both approaches: virtualize enough workload to reduce immediate risk, then modernize the most important or expensive workloads.
Where the technology fits in Snowflake AIM
Snowflake now presents Datometry as part of Snowflake AIM, short for AI-driven Modernization and Virtualization. AIM combines Teradata virtualization, data-warehouse migration and Spark workload modernization. Snowflake says the framework builds on Datometry, SnowConvert AI and Snowpark Migration Accelerator.
The Snowflake AIM documentation distinguishes the two central paths:
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- Virtualization: run existing workloads on Snowflake without rewriting everything first.
- Modernization: convert code, data and pipelines for native Snowflake execution through phased validation.
This makes the deal more significant than a standalone product acquisition. Snowflake is positioning migration as an integrated platform capability covering assessment, compatibility, conversion, testing and eventual cutover. Its broader modernization overview is at Snowflake’s modernization page.
Datometry versus SnowConvert AI
SnowConvert AI is primarily a code-conversion and migration-assistance tool. Snowflake documentation says it converts source definitions and code such as tables, views, stored procedures and functions into Snowflake SQL, then identifies errors, warnings, issues and functional differences for review.
For Teradata, the documented support matrix includes tables, views, stored procedures, functions, BTEQ, MultiLoad and TPump. The same matrix describes extraction scripts rather than a direct source-database connection, data migration, Snowflake deployment or guaranteed automatic remediation. Capabilities can change, so teams should verify the current documentation at SnowConvert AI documentation.
SnowConvert AI and Datometry are complementary: one helps rewrite and modernize code; the other is designed to keep existing workloads operating while modernization proceeds. Snowpark Migration Accelerator addresses Spark-oriented migrations toward Snowpark for Python, Scala and Java scenarios, as described at its documentation page.
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What customers can use now
On April 30, 2026, Snowflake announced the public preview of Datometry for Snowflake, specifically positioned for Teradata customers seeking a faster exit, less immediate rewriting and incremental conversion to native Snowflake SQL. The announcement is at Datometry for Snowflake.
Public preview is not the same as general availability. Preview features may have changing interfaces, limited support, restricted production use or incomplete coverage. The current AIM documentation should be checked before committing a production migration; do not assume every Datometry capability is generally available.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the “four times faster” and “90% cheaper” claims mean
Snowflake says Datometry can make certain migrations up to four times faster and reduce costs by up to 90%. These are Snowflake’s claims; the announcement did not provide an independent benchmark or methodology.
Actual results will depend on workload complexity, Teradata features, data volume, transfer and network constraints, the duration of parallel operation, validation effort, Snowflake consumption, tuning and professional-services requirements. A reduction in rewrite labor is not automatically a reduction in total ownership cost.
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SnowConvert AI’s Migration Assistant also has usage economics separate from project labor. Snowflake’s billing documentation gives an illustrative estimate of approximately $0.027 for a 3,500-token interaction under specified Enterprise Edition and AWS US East conditions; it is not a migration quote. Details are provided at SnowConvert billing documentation.
What virtualization does not solve
- Data movement, synchronization and reconciliation.
- Identity, security, governance and network architecture.
- Performance, concurrency, workload isolation and service-level objectives.
- Backup, recovery, monitoring and operational ownership.
- Unsupported Teradata syntax, utilities, procedures or external dependencies.
- Snowflake compute, storage, transfer and parallel-run costs.
- The eventual work of removing legacy logic and the compatibility layer.
A translated query is not proof of equivalent results or performance. Every production workload still needs workload-specific testing, observability and a rollback plan.
Questions a migration team should answer before committing
- Which SQL constructs, macros, stored procedures, BTEQ, MultiLoad and TPump flows are supported?
- Can existing applications connect through standard drivers without modification?
- How are temporary or volatile tables, session semantics, errors and transaction behavior handled?
- What latency does translation add, and how are query plans and workload-management rules mapped?
- Can migration proceed by application, schema or business domain?
- How will changes be synchronized during the transition and results compared between platforms?
- What is the rollback process and the cost of operating both systems?
- What preview limitations, support terms and availability commitments apply?
Who should consider it—and who should not assume it is enough
Strong candidates
- Teradata customers approaching a costly renewal.
- Enterprises with many mission-critical or poorly documented applications.
- Organizations that cannot tolerate a large outage or a single big-bang rewrite.
- Teams seeking to reduce legacy infrastructure first and modernize selectively.
Cases requiring another approach
- Organizations committed to an immediate redesign of data models, governance or semantic layers.
- Teams seeking to eliminate all legacy logic rather than preserve behavior temporarily.
- Small, simple estates where enterprise migration tooling would add unnecessary overhead.
- Projects needing a turnkey data-transfer, validation and governance program.
- Decision-makers requiring independently verified guarantees of speed or cost.
Bottom line
Snowflake’s Datometry transaction is best understood as a move to make Teradata migration incremental: virtualize existing workloads first, reduce disruption, and modernize selectively afterward. The technology may shorten the path to Snowflake, but it does not remove data validation, operational engineering, cost modeling or the eventual decisions about which legacy code should be rewritten.
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