Data Migration Tools: Meet the Snowflake AIM Application

Data Migration Tools: Meet the Snowflake AIM Application

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Oct 21, 2026 / 10 min read Product and Technology Copy post link Open in Claude Open in ChatGPT

The Snowflake AIM Application: Data Migration Gets Its Own Front Door

Federico Zoufaly

Database migrations have always been one of those projects that everyone needs to happen and nobody volunteers to run. The tooling is fragmented and the risk tolerance is zero. Teams spend weeks stitching together data migration tools: extraction scripts, conversion tools, deployment pipelines and validation jobs — and then spend another few weeks wondering if they got it right.

We set out to change that with Snowflake AIM. It started as a skill inside the Snowflake CoCo CLI, then arrived in CoCo Desktop. As of Oct. 20, AIM has a surface entirely its own: the Snowflake AIM application, now in public preview.

With the AIM application, migration engineers have many new advantages:

Zero setup, zero prerequisite knowledge: As long as you have the standard CoCo CLI and Snowflake CLI available, just open the app and start. No SCAI CLI to install, no IDE to configure, no terminal commands to memorize. Dependencies install themselves on the first run. You need to update the app? Just click on update directly from it.

Guided end-to-end workflow: You don't need to know the right sequence of a migration. The application walks you through every stage — from connecting to your source database through validated deployment on Snowflake.

Migration-focused UX: No distractions from unrelated IDE or development features. The entire interface is organized around the migration lifecycle. Everything you need is visible; nothing you don't need is in the way.

Persistent project state: Close the app, come back days or weeks later. The agent picks up exactly where you left off. No reorientation tax, no lost progress, no "where were we?"

Deterministic conversion with AI remediation: You get the reliability of our five-year-proven deterministic code conversion engine for the baseline, plus AI that fills in the gaps — explaining issues, suggesting fixes and generating tests. Every fix can become a reusable rule, and every rule can propagate across every similar object in the project, so the migration gets smarter as you work. That's how teams avoid fixing the same pattern 200 times.

Automated testing and validation: Source-side baselines, two-sided comparison, automated fix loops. Data validation and code testing are handled as separate concerns so you always know what you're looking at.

Multi-user collaboration: Multiple team members can work on the same project simultaneously without duplicating effort.

Full CoCo power underneath: For advanced users, the entire agentic platform is accessible — custom rules, MCP tools, custom skills, deployment wave tuning. The guided path is a fast lane, not a cage.

Broad platform coverage: SQL Server, Redshift, Oracle, Teradata, PostgreSQL out of the box, with Snowflake AIM supporting 15+ dialects and ETL conversion for SSIS and Informatica.

Same migration engine, new experience

Under the covers, the core engine is unchanged: It powers the same deterministic code conversion, dependency-aware deployment waves and advanced agentic test-fix loops — spanning both deterministic and AI-driven validation — that make a migration project smarter as it progresses. What changed is how you reach it.

The AIM application is a standalone desktop application built for migration projects and nothing else. You open it, point it at your source database, and a guided workflow carries you through each stage without an IDE to configure or a terminal to drive.

The CoCo CLI and CoCo Desktop remain excellent surfaces for AIM if you already work in them. The application is a purpose-built third option where the migration workflow sits front and center. With the AIM application, migration engineers always know what assistance to expect from the AIM agent, including a guided setup for each phase, clear visibility into the status of the migration project and precise indicators of where human judgment is needed to assist AI. An engineer driving a migration gets every stage laid out in order, with state tracked and the freedom to pause and resume. A DBA or data engineer who inherited the project without migration being their day job gets structure instead of a blank prompt. Migration engineers retain full control through the underlying agentic loop — customizing rules, writing fix patterns, reordering waves and building test cases. Meanwhile, project stakeholders get instant visibility into progress, object counts and validation results without digging through log files.

Figure 1: CoCo CLI and CoCo Desktop AIM experiences: Just start with a simple prompt like “Help me convert my SQL Server database.”

Figure 2: The curated experience of the AIM application: an AI-driven specialized workflow that contains years of experience executing migrations.

What the AIM application actually does

The application guides you through six stages of a migration to Snowflake:

Connect: Set up a connection to your source database.

Initialize: Create a local migration project with the scaffolding it needs.

Register: Extract data definition language (DDL) and stored procedures from the source, or import existing .sql files.

Convert: Translate source SQL to Snowflake SQL through the deterministic code conversion engine.

Assess: Generate an interactive report covering complete end-to-end lineage and the migration plan assisted by multiple specialized reports.

Migrate: Deploy the code, move the data and validate the result. This is where most of the work lives. Test-fix AI loop: AI-assisted remediation handles what the deterministic pass cannot. AIM generates test cases and synthetic data, runs the converted code to verify its accuracy, and AI fixes any issues.

Inside the migrate phase

Stages one through five set the table. Stage six covers four workstreams, each with its own logic, validation and fix loops. The agent coordinates all of them while keeping them distinct, because they could fail in different ways.

The validation and fix loop deserves a more in-depth discussion. In fact, testing is three problems, not one

Confirming that rows landed correctly is a different job from confirming that a stored procedure returns what it returned...