Modernize‑your‑code is a Microsoft open‑source "solution accelerator" designed to streamline the migration of SQL codebases from legacy systems to modern data environments. Its core purpose is to address the challenges organizations face when updating outdated SQL queries—such as missing documentation, obsolete dialects, and loss of business context—by automating translation to current SQL dialects with AI agents ReadMe.
1. The Challenge: Legacy SQL Code Maintenance
Organizations routinely wrestle with:
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Obsolete dialects (e.g., Informix‑SQL, older PL/SQL versions),
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Absent documentation, and
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Lost institutional knowledge about business logic.
Updating this legacy SQL manually is slow, error‑prone, and demands rare legacy‑system expertise. That's where this accelerator helps blogs.incyclesoftware.com+3trendshift.io+3TechTarget+3LinkedIn+6GitHub+6blogs.incyclesoftware.com+6.
2. What It Is and How It Works
This accelerator lets users feed in a batch of SQL queries, specify the desired target dialect (e.g., T‑SQL for Azure SQL, PostgreSQL), and initiate a pipeline powered by Azure AI Foundry, Azure OpenAI Service, Container Apps, Cosmos DB, and Azure Storage LinkedIn+3GitHub+3trendshift.io+3.
Key elements include:
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A multi‑agent architecture, where specialized LLM agents collaborate to translate, validate, optimize, and capture business logic.
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A batch processing pipeline—SQL queries are taken from storage, transformed, reviewed, and then outputted in the new dialect.
This modern approach ensures consistency, efficiency, and traceability across translation GitHub+2GitHub+2trendshift.io+2.
3. Core Features & Advantages
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Code language modernization: Updates SQL dialect compatibility, reducing reliance on outdated skill sets GitHub.
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Summaries and code reviews: Agents generate human‑readable explanations and flag issues, enabling manual review.
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Business logic extraction: Insights into the logic in legacy queries help preserve data integrity.
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Automated, iterative transformation: Batch processing with error testing increases reliability and speeds up migrations GitHub.
4. Architecture Overview
Two deployment models are supported:
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Sandbox configuration: Ideal for development and initial exploration.
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WAF‑aligned configuration: Integrates Azure’s Well‑Architected Framework for production‑grade deployments ReadMe+4Microsoft Learn+4code.stanford.edu+4GitHub.
The architecture uses:
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Azure AI Foundry: Orchestrates LLM agents.
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Azure OpenAI Service: Handles querying and translation.
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Azure Container Apps: Hosts serving components.
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Azure Cosmos DB: Stores agent metadata and audit trails.
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Azure Storage: Temporary storage for SQL batches TechTarget+5GitHub+5trendshift.io+5.
Agents orchestrated via the Semantic Kernel Agent Framework ensure each query goes through translation, optimization, validation, and metadata extraction stages.
5. Quick Deploy Guide
A "Quick Deploy" mode simplifies setup via an Azure deployment guide that integrates with GitHub Codespaces or Dev Containers GitHub+1trendshift.io+1.
Important considerations:
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Azure OpenAI quota: Verify your subscription limits before deploying.
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Required roles: You need contributor‑level permissions in Azure.
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Service availability: Ensure your target region supports needed services (e.g., East US, UK South, Japan East) Microsoft Learn+2GitHub+2trendshift.io+2.
6. Cost and Dependencies
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Mostly usage‑based: Azure Container Registry has fixed registry fees; other services charge by usage GitHub+1trendshift.io+1.
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Estimate pricing via the Azure Calculator.
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Prerequisites: Contributor role, OpenAI quota, regional service availability trendshift.io.
7. Business Value
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Faster migrations: Automates labor‑intensive manual translation.
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Reduced errors: AI‑driven validation ensures accuracy and preserves logic.
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Legacy‑knowledge retention: The process documents translated queries and the logic within GitHub+2GitHub+2LinkedIn+2.
8. Foundational Research & Ethical Considerations
The repo references a multi‑agent AI research paper underpinning its methodology. A Responsible AI Transparency FAQ guides users on ethical use. Licensing is MIT; standard disclaimers apply, including export restrictions and non‑medical, non‑financial advising disclaimers GitHub.
9. How to Get Started
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Clone or use the quick‑deploy pipeline.
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Confirm OpenAI quota and Azure permissions.
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Prepare your batch of SQL queries and select the target dialect.
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Deploy the solution on your subscription.
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Monitor the pipeline, review translated output, iterate as needed.
10. Related Accelerators
Microsoft offers similar tools for:
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Document knowledge mining,
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Conversation analytics,
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Multi‑agent automation engines TechTarget+8GitHub+8trendshift.io+8trendshift.io.
Final Thoughts
This accelerator provides a clear, AI‑driven framework to streamline the modernization of SQL assets. By combining agent‑style translation, validation, and business‑logic extraction, it enables safer, faster migrations with less manual effort. With multi‑dialect support and integration into Azure, it’s a valuable tool for any data estate modernization roadmap.
