A 12-Week Roadmap to Migrate from SQL Server to Snowflake
A structured 12-week migration from SQL Server to Snowflake unlocks scalability, cost efficiency, and modern analytics—empowering enterprises to build a future-ready data ecosystem.
Edrin Thomas
Founder & CTO
- 3 mins read
- 12 Sep, 2025
Modern enterprises are moving from legacy data warehouses like Microsoft SQL Server to cloud-native platforms such as Snowflake. Why? Because Snowflake offers scalability, cost efficiency, and support for advanced analytics and machine learning pipelines. In this blog, we’ll walk you through a structured 12-week migration plan to transition from SQL Server to Snowflake seamlessly.
Why Migrate from SQL Server to Snowflake?
- Scalability: Snowflake scales compute and storage independently.
- Cost Savings: Pay only for what you use with transparent credit-based pricing.
- Cloud-Native Flexibility: Built-in support for modern analytics, AI/ML, and real-time data pipelines.
- Simplified Management: No server patching, maintenance, or tuning required.
Migration Overview
Our proven approach organizes the migration into three phases over 12 weeks:
- Pre-requisites & Planning (Weeks 1–3)
- Migration Preparation & Execution (Weeks 4–9)
- Post-Migration Activities (Weeks 10–12)
Phase 1: Pre-requisites & Planning (Weeks 1–3)
Before moving data, align stakeholders, assess the current environment, and set up Snowflake.
- Kickoff & Strategy: Define goals, success metrics, and assign roles.
- Inventory & Assessment: Catalog databases, schemas, stored procedures, reports, and dependencies.
- Snowflake Setup: Provision Snowflake on Azure, configure security (MFA, SSO), and set cost monitors.
Phase 2: Migration Preparation & Execution (Weeks 4–9)
This is where databases, code, and data begin moving to Snowflake.
- Schema Conversion: Translate SQL Server schemas into Snowflake equivalents (tables, views, data types).
- Code Migration: Refactor T-SQL stored procedures and scripts for Snowflake SQL.
- Data Migration: Perform a bulk load of historical data followed by incremental pipelines to sync live data.
- Validation & Testing: Compare row counts, aggregates, and reports across SQL Server and Snowflake.
- BI Integration: Repoint SSRS/Power BI reports to Snowflake and conduct user acceptance testing.
Phase 3: Post-Migration Activities (Weeks 10–12)
Final checks and optimizations ensure Snowflake is production-ready.
- System Testing: Simulate workloads and validate real-time pipelines.
- Performance Tuning: Optimize virtual warehouses, queries, and data pipelines.
- Training & Governance: Train teams on Snowflake best practices, establish access controls, and set monitoring alerts.
- Go-Live Prep: Finalize cutover checklist and sign off on the proof of migration.
Best Practices for a Smooth Migration
- Use automated tools like Snowflake SnowConvert for schema/code conversion.
- Implement Azure Data Factory or Snowpipe for reliable data pipelines.
- Establish strong data governance early (role-based access, monitoring, cost controls).
- Engage business users in UAT to build confidence in the new system.
Conclusion
Migrating from Microsoft SQL Server to Snowflake is not just a technical upgrade—it’s a strategic move toward scalability, agility, and future-ready data capabilities. By following a structured 12-week roadmap, enterprises can minimize risk, ensure seamless data transition, and unlock Snowflake’s full potential for advanced analytics, real-time pipelines, and cost efficiency. With the right planning, tools, and governance, your business can confidently step into a modern cloud-native data ecosystem.
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