Connect your legacy database or ETL estate and receive a complete migration blueprint in 3–5 days — every object scored, every pipeline mapped, your entire Fabric architecture designed before a single line moves.
The estate is bigger than expected, documentation is missing, and certified Fabric engineers are genuinely scarce. Most teams don't know what they're dealing with until it's too late.
Select your current platform to see exactly what AnyToFabric migrates and where it lands in Microsoft Fabric.
AnyToFabric converts your entire ETL estate — every job, script, and workflow assessed, complexity-scored, and converted to Data Factory pipelines or Spark Notebooks.
AnyToFabric is not a generic statement of work. Each capability is a deliverable with specific outputs your team can act on immediately.
A structured process that replaces months of ad-hoc migration work with a defined timeline and specific deliverables at each stage.
Direct connection to your source platform. Full inventory of every object, complexity scoring, and a Fabric Readiness Score.
Medallion layer design, Lakehouse vs Warehouse placement, workspace structure, and a wave-based migration roadmap.
Automated code conversion with engineer review of flagged objects. SQL becomes T-SQL, ETL jobs become Data Factory pipelines.
Parallel-run row-level validation, Power BI rebuilt on Fabric, full handover with runbooks and team training.
| Object Type | Total | Auto-Migrate | Manual Review | Complexity | Coverage |
|---|---|---|---|---|---|
| Tables | 4,200 | 4,116 | 84 | Low | |
| Views | 1,860 | 1,674 | 186 | Medium | |
| Stored Procedures | 1,820 | 1,092 | 728 | High | |
| ETL Pipelines | 460 | 322 | 138 | Medium | |
| Functions | 300 | 252 | 48 | Low |
Each source object is mapped to a Fabric target zone based on workload type, query pattern, and team capability. Structured reporting workloads go to Fabric Warehouse (T-SQL). Spark-based and semi-structured workloads go to Fabric Lakehouse (Delta).
| Workload | Fabric Target | Format | Rationale |
|---|---|---|---|
| Core DW Tables | Fabric Warehouse | T-SQL DDL | SQL-first reporting, existing BI tooling |
| ETL Staging | Fabric Lakehouse | Delta + Spark | Bulk loads, Spark-native processing |
| Historical Archive | Fabric Lakehouse | Parquet / Delta | Cost-effective cold storage on OneLake |
| Reports & Dashboards | Power BI | DAX / DirectLake | Rebuilt semantic models on migrated data |
Source and target run concurrently for 48 hours. Row counts and aggregates compared at object level. Each object receives a PASS, WARN, or FAIL status before sign-off.
Your three realistic options: Exillar AnyToFabric, a large system integrator, or attempting the migration in-house. Here's what that comparison actually looks like.
| Factor | Exillar AnyToFabric | Large System Integrator | In-House Team |
|---|---|---|---|
| Microsoft Partner status | Official Microsoft Partner on Fabric, Synapse, ADF | Varies by firm | Not applicable |
| DP-600 certified engineers | Multiple certified on every engagement | May be available, not guaranteed | Scarce — months to hire |
| Snowflake Partner | Handles Snowflake toFabric migrations | Rarely both | Not applicable |
| Power BI / post-migration BI | Dashboards rebuilt as part of delivery | Usually separate engagement | If team has capacity |
| Time to assessment blueprint | 3–5 days | 5–8 weeks | 2–4 months |
| Parallel-run validation | Row-level, object-by-object sign-off | Manual spot-checks | Rarely structured |
| Cost structure | Competitive Ahmedabad delivery model | US/UK day rates | Staff cost + extended timeline |
| Geographic coverage | UK, Europe, Middle East, US | Global | Internal only |
Estimates are based on typical enterprise migration projects. Actual timelines depend on schema complexity, data volumes, and internal readiness.
We connect to your source platform, inventory every object, and return a Fabric Readiness Score with a wave-based migration roadmap. Fixed scope. No obligation to proceed.