Migration Risk Score
72 / 100
High migration risk driven by procedural logic and dependency complexity.
Sample Output
See what your migration assessment could look like
This sample report shows the kind of output SQL2Snow is designed to produce: migration risk score, top cost drivers, level of effort estimate, and a phased migration plan for SQL Server to Snowflake.
Runs locally
No data leaves your environment
Designed for enterprise migration planning
Estimated Effort
5–7 months
Directional estimate for an initial enterprise migration wave.
Confidence Level
Medium
Estimate should be validated with business rules, testing scope, and integration detail.
Recommended Approach
Phased migration
Prioritize foundation objects first, then procedural refactoring and testing.
What this report is designed to answer
Enterprise teams usually do not need a raw object dump first. They need to know where migration risk is concentrated, what will drive effort, and how to approach the work.
Where is the risk?
- Stored procedure complexity
- Cross-database dependencies
- Testing and validation burden
What will drive effort?
- Procedural refactoring
- Dependency sequencing
- Data reconciliation and QA
How should the migration start?
- Foundation-first planning
- Wave-based execution
- Clear assumptions and caveats
Sample report preview
The example below uses representative sample data to illustrate how findings can be summarized for both technical and leadership audiences.
Executive summary
Risk Score
72 / 100
High
Estimated Effort
5–7 months
Initial migration wave
Top Driver
Stored procedures
Heavy procedural footprint
Recommendation
Phased plan
Refactor high-risk logic early
System overview
| Category | Count | Observation |
|---|---|---|
| Databases analyzed | 3 | Moderate estate with shared dependencies |
| Tables | 420 | Mostly straightforward, with a subset of wide tables |
| Views | 180 | Several layered views increase translation complexity |
| Stored procedures | 260 | Main source of migration risk and LOE variability |
| Functions | 90 | Requires targeted review for rewrite patterns |
Complexity distribution
Top migration cost drivers
| Driver | Impact | Why it matters |
|---|---|---|
| Stored Procedure Complexity | High | Complex procedural logic often needs redesign for Snowflake execution patterns. |
| Cross-Database Dependencies | High | Dependency chains complicate sequencing, validation, and rollout planning. |
| ETL Orchestration Gaps | Medium | Upstream and downstream orchestration may require redesign or migration in parallel. |
| Testing Scope | Medium | Data reconciliation, business logic validation, and UAT are commonly underestimated. |
Effort breakdown
| Workstream | Estimated effort |
|---|---|
| Schema migration | 3–4 weeks |
| Stored procedures and functions | 8–12 weeks |
| Data validation | 4–6 weeks |
| Testing and reconciliation | 6–8 weeks |
| Contingency / unknowns | 2–3 weeks |
Interpretation
The effort profile is not driven mainly by table translation. It is driven by procedural logic, dependency sequencing, and validation overhead.
- High-risk objects should be isolated early
- Testing can consume 30–40% of effective effort
- Confidence increases with better dependency and business rule detail
Phased migration plan
Phase 1 — Foundation
Migrate core schemas, low-risk tables, and required platform groundwork.
Phase 2 — Pipelines and dependent objects
Establish data movement patterns and address the first layer of operational dependencies.
Phase 3 — Procedural refactoring
Rework high-complexity procedures, functions, and execution patterns for Snowflake.
Phase 4 — Validation and cutover readiness
Execute reconciliation, testing, stakeholder signoff, and production transition planning.
Key recommendations
- Do not treat stored procedures as a late-stage cleanup task.
- Map cross-database dependencies before detailed wave planning.
- Budget explicitly for validation, QA, and business signoff.
- Use phased delivery rather than one-shot migration for this profile.
What this sample is showing
- How migration findings can be summarized for leadership
- How technical drivers translate into cost and effort
- How risk scoring can support planning discussions
- How a phased migration recommendation can be framed
What a real assessment would add
- Environment-specific object inventory
- Detailed feature findings and complexity flags
- More tailored LOE assumptions
- Project-specific migration sequencing guidance
SQL2Snow is focused on SQL Server to Snowflake migration assessment, effort estimation, and planning support. This page shows illustrative sample output for product demonstration purposes.