What is SnowConvert?

Migrating legacy data is a high-stakes process—it’s not just about moving SQL code; it’s about ensuring every data point, transformation, and dependency makes it over correctly. 

SnowConvert, built by Snowflake, is positioned as a free, self-serve migration tool for translating SQL from databases like Teradata, Oracle, and SQL Server into Snowflake-compatible formats. If you’re migrating to Snowflake, that sounds great—but what exactly does SnowConvert do, and where does it fall short?

At its core, SnowConvert is a SQL translator. It converts database objects from a limited set of legacy systems to Snowflake SQL and performs schema-level validation, but it doesn’t actually ensure that the migration is complete and accurate. 

SnowConvert’s limitations include:

• No granular, value-level data diffing: It checks schema structure but doesn’t verify if actual data values match between source and target.
• No AI-powered refinement: There is no “translate, diff, repeat” feedback loop powered by advanced AI or large language models to fine-tune conversions until complete parity is achieved.
• No validation beyond SQL: It focuses on SQL code and doesn’t validate ETL workflows or stored procedures.
• Limited source database support: SnowConvert only works with Teradata, Oracle, SQL Server, and Redshift.
• SQL-only conversion: It attempts a 1-to-1 SQL transformation but doesn’t support modern workflow tools like dbt.
• Lack of collaboration & reporting tools: SnowConvert runs as a standalone desktop application, generating static, hard-to-share reports, which is an impractical setup for most data migrations.

For simple SQL translation, SnowConvert may be enough. But in migrations, data accuracy matters just as much as code correctness. Without validation, there’s no way to know if errors were introduced. A migration tool needs to ensure every record is correctly migrated, business logic is preserved, and ETL pipelines remain intact.

That’s where Datafold’s Migration Agent (DMA) comes in. Unlike SnowConvert, DMA doesn’t just rewrite SQL—it ensures every record is migrated correctly, every transformation is validated, and every workflow remains intact. And, DMA is data warehouse agnostic and stands out for its true automation. 

In this article, we’ll break down how SnowConvert works, its limitations, and how it compares to Datafold’s Migration Agent (DMA), a tool built for fully automated, high accuracy data migrations.

How does SnowConvert work?

SnowConvert ingests legacy SQL code from Teradata, Oracle, Redshift, or SQL Server and converts it into Snowflake SQL.

We pieced together its capabilities from product descriptions and published docs to understand what it does well, where it has limitations, and how it stacks up against Datafold’s Migration Agent. (If you’re ready to get into the technical weeds, check out DMA’s docs or this blog that looks into how DMA works). 

Here’s a breakdown of its key components to understand exactly what SnowConvert does—and what it doesn’t do.

SnowConvert features and benefits

SnowConvert’s docs describe how it converts code into Snowflake SQL:

• Code parsing: SnowConvert begins by analyzing SQL syntax using abstract syntax trees (ASTs) to identify database objects. This allows it to identify key database objects for conversion; however, it focuses solely on the structural aspects of the code rather than the actual data content.
• Configuration-based transformation: It applies predefined rules to convert tables, views, and stored procedures into Snowflake’s SQL dialect. While this automates the basic conversion process, it does not employ iterative refinement to automatically fine-tune the output until full parity is achieved.
• Schema-level validation: SnowConvert performs validation at the schema level by checking for syntax consistency and adherence to Snowflake’s standards, and it generates detailed reports that list any conversion errors or issues. However, this validation is limited to schema and syntax checks and does not extend to granular, value-level data diffing or comprehensive validation of non-SQL components like ETL workflows.
• Integration: After conversion, SnowConvert produces output files and detailed reports that document any errors or issues, which must be manually reviewed and corrected. The tool is designed primarily for integration within the Snowflake ecosystem rather than providing an end-to-end migration workflow.
• Self-serve model: SnowConvert is offered as a free, self-serve solution, enabling users to run the conversion process on their own. However, its interaction is mostly command-line or through basic interfaces, meaning it does not include a comprehensive, interactive dashboard for full migration management.

What’s missing?

SnowConvert follows a one-time, rule-based process—if the converted SQL isn’t accurate, it’s your job to fix it. More importantly, it does nothing to check whether the actual data in the new system matches the original.

This makes it useful for small-scale or sample syntax conversions but insufficient for full-scale migrations.

SnowConvert migration solution pros and cons

SnowConvert’s strength lies in its no-licensing-fee model and its ability to convert legacy SQL code from platforms like Teradata, Oracle, Redshift, and SQL Server into Snowflake-compatible formats using configuration-based transformation and schema-level validation. 

For teams needing SQL code conversion and nothing more, SnowConvert works. But for full migration validation and automation, it leaves too many gaps.

While SnowConvert handles basic code conversion well, it does not offer iterative refinement or deep, value-level data diffing—and it’s limited solely to migrations into Snowflake. This matters because many legacy systems include complex ETL workflows and non-SQL components, which are critical for ensuring that all data and processes migrate accurately. 

Perhaps the biggest challenge is how SnowConvert operates. It runs as a standalone desktop application, generating static reports that aren’t designed for large-scale collaboration. For enterprise migrations, where multiple teams need to track errors, validate data, and share findings in real time, this setup is a major bottleneck. Instead of a centralized dashboard for monitoring progress, teams are left managing migration insights manually—an impractical approach when dealing with thousands or millions of records.

SnowConvert isn’t built to scale, and without real collaboration or reporting tools, it becomes difficult to ensure an accurate, fully validated migration.

Comparing Datafold and SnowConvert

While SnowConvert handles SQL conversion, it doesn’t address the broader challenges of data validation, iterative refinement, or non-SQL migration elements.

Datafold’s Migration Agent (DMA) takes a more comprehensive approach—automating not just code conversion but also value-level validation and iterative improvement. DMA translates SQL, stored procedures, and ETL workflows, then systematically verifies that every record in the new system matches the original. Using an AI-powered “translate, diff, repeat” loop, DMA continuously refines its translations, reducing manual debugging and ensuring full parity between source and target.

Here’s the head-to-head comparison:

Why a comprehensive AI-driven approach matters

SnowConvert provides a functional, rule-based solution for SQL translation, but in complex migrations, code conversion is only part of the problem. 

Ensuring that every data point is correctly migrated is just as critical as translating SQL syntax. Without value-level validation, there’s no way to know if transformations introduced errors.

Datafold’s Migration Agent (DMA) is designed to solve this problem. Instead of just translating code, DMA ensures that every record, every workflow, and every dependency is validated and accurate.

How Datafold’s approach works

Unlike SnowConvert’s static, rule-based process, DMA is built for continuous improvement. Its AI-powered system not only translates legacy SQL but also performs deep validation at every step:

1. Freezing the source data: DMA locks in a snapshot of the input datasets, ensuring consistent inputs across the source and target environments.
2. AI-powered code translation: Unlike static rule-based conversion, DMA iteratively refines SQL translations based on feedback.
3. Value-level validation: DMA compares every record post-migration, verifying accuracy at the most granular level.
4. End-to-end automation: DMA validates ETL workflows, stored procedures, and full pipeline transformations, reducing reliance on manual review.

Once data alignment is complete, Datafold’s AI engine translates legacy SQL, stored procedures, and ETL workflows into the target format. But the process doesn’t stop there.

The “translate, diff, repeat” cycle

DMA follows an iterative “translate, diff, repeat” cycle, powered by advanced AI and large language models:

1. Our AI translates legacy code into the target system
2. DMA performs granular, value-level data diffing, comparing every record in the source and converted output
3. If discrepancies are found, DMA automatically refines the translation and runs the process again
4. This loop continues until perfect data parity is achieved

This continuous refinement process—going beyond basic syntax translation to include non-SQL components like ETL workflows and stored procedures—is what sets Datafold apart from SnowConvert.

By automating the entire migration workflow and providing detailed, auditable reports on data lineage, DMA minimizes manual intervention, prevents silent data errors, and speeds up migration timelines by a factor of 6. 

Choosing the right migration tool

The cloud data wars are in full swing. Since Snowflake announced SnowConvert and Databricks acquired BladeBridge, cloud vendors have been offering free migration tools to make it easier to move into their ecosystems. 

But free doesn’t mean complete, especially if they focus on code conversion, not complete migrations. A migration tool needs to do more than rewrite SQL—it must validate the data itself and ensure that business logic and workflows remain intact. 

SnowConvert handles SQL translation, but a real migration requires full data validation. Without it, teams risk:

• Silent data discrepancies that cause downstream issues
• Broken workflows and dependencies
• Time-consuming manual debugging
• Migrations going entirely wrong

This is where Datafold’s AI-driven Migration Agent (DMA) stands out. DMA automates the entire migration workflow with an iterative “translate, diff, repeat” cycle powered by advanced AI. This ensures every record is validated at a granular level, offering full data lineage transparency and significantly reducing manual intervention and error risk.

While free tools lower the entry cost, the risk of incomplete or inaccurate migrations can be far more expensive in the long run. If you’d like to see how DMA can accelerate your migration while ensuring complete accuracy, book time with our team.