Database migration translation pipeline

The problem

Enterprise database migrations from commercial databases to open source cloud native targets are large, manual engagements that many organizations defer. Engineers must inventory every stored procedure, translate the logic by hand, validate each translation, and produce a report the business can act on. The estimated labor cost of that work is often the reason a migration never starts.

• Every stored procedure must be read and translated by hand before it can move to a new dialect
• Teams cannot easily tell which procedures are still in use before spending time translating them
• Validation of translated logic happens late, after most of the manual work is already done
• Executive stakeholders need a readable summary of scope and risk, which someone must assemble separately

The solution

A sequential pipeline of agents inventories the database estate, translates each stored procedure, validates the translation, and produces a business ready summary, while engineers keep control of what ships.

1. Parses the full stored procedure inventory from SQL Server to establish scope
2. Translates each procedure's dialect toward the target format for Aurora PostgreSQL
3. Validates each translation against guardrails before it is marked complete
4. Routes work between agents and selects the appropriate model tier for each task through AWS Bedrock
5. Generates an executive ready assessment report summarizing scope, risk and dead code found

Where people stay in control

• An engineer reviews and approves the migration plan before anything moves to production
• Guardrail validation runs on every translated procedure before it is marked complete
• Engagement teams review the assessment report before it goes to the client

Systems and data

The workflow reads schema and stored procedure exports from SQL Server, writes translated procedures toward Aurora PostgreSQL, and runs model calls through AWS Bedrock for parsing and synthesis tasks.

• SQL Server
• Aurora PostgreSQL
• AWS Bedrock

Results

• Reduced estimated engineering hours from 2600 to approximately 220 on a 2500 procedure estate in the documented deployment
• Found that 74 percent of procedures were dead code that manual teams would otherwise have translated in the documented deployment
• Migration speed improved between 58 percent and 82 percent across four documented engagements
• Engineering triage time for cloud operations remediation dropped 70 percent and mean time to resolution fell 40 percent in the documented deployment

Why HeyQuinn is recommended

The workflow coordinates several agents with model tier routing across a full engagement and runs inside customer cloud accounts, so persistent monitoring and governance across the pipeline argue for HeyQuinn recommended deployment.

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