Modernizing an enterprise analytics platform by moving from Synapse to Microsoft Fabric
A structured migration program that assessed the existing Synapse environment, redesigned workloads for Fabric, migrated data and analytics assets, and established a controlled path to the new platform.
The existing Synapse environment had become expensive to maintain and difficult to evolve at enterprise scale.
The organization had years of analytics workloads built across Synapse SQL, Spark, pipelines and Power BI. Moving to Fabric required more than copying tables. Workloads, dependencies, performance patterns and reporting assets had to be assessed and redesigned where necessary.
What we found
- More than 3,200 tables and views across the Synapse environment.
- Multiple SQL and Spark workloads with different performance characteristics.
- Hundreds of pipelines with dependencies across source and reporting layers.
- Power BI reports and semantic models tightly coupled to existing datasets.
- Legacy and unused workloads increasing migration scope.
- Different workloads required different Fabric target patterns.
- Limited visibility into the true business criticality of every asset.
What the business needed
- A migration roadmap based on business value and technical complexity.
- Minimal disruption to business reporting and analytics.
- Clear mapping from Synapse workloads to Fabric capabilities.
- Performance validation before production cutover.
- Controlled migration of data, pipelines and Power BI dependencies.
- Cost and capacity management for the new Fabric environment.
- A repeatable migration factory for future workloads.
We treated the migration as a workload modernization program, not a simple platform copy.
Every asset was assessed, classified and mapped to the most appropriate Fabric architecture. High value workloads were migrated first, while complex workloads were redesigned and validated before cutover.
Synapse to Fabric migration approach
The team established a migration factory covering discovery, assessment, remediation, migration, validation and production cutover.
- Inventoried Synapse tables, views, stored procedures, Spark jobs, pipelines and Power BI dependencies.
- Classified assets by usage, business criticality, complexity and migration readiness.
- Mapped workloads to Fabric Lakehouse, Warehouse, Data Factory and Power BI patterns.
- Converted and optimized SQL, pipelines and Spark workloads where required.
- Validated data reconciliation, performance, security and report outputs.
- Executed phased cutover with rollback controls and post migration monitoring.
A controlled migration path from Synapse workloads to the Fabric analytics platform
The target architecture used Fabric capabilities according to workload needs rather than forcing every workload into one storage or processing pattern.
A six stage migration factory designed to reduce risk and accelerate adoption
The migration was delivered in controlled waves, with each wave passing technical and business validation before production cutover.
Discover
Inventory workloads, dependencies, usage, criticality and technical characteristics.
Assess
Score assets for complexity, readiness, value and target Fabric pattern.
Design
Define target Lakehouse, Warehouse, pipeline and reporting architecture.
Migrate
Move data and workloads while converting code and redesigning complex components.
Validate
Reconcile data, test performance, validate reports and complete user acceptance.
Cutover
Release production workloads, monitor usage and retire legacy Synapse components.
The migration reduced platform complexity while improving the speed and economics of analytics delivery.
Lower platform effort
Standardized Fabric capabilities reduced the operational effort required to manage the analytics environment.
Faster workload delivery
Reusable Fabric patterns accelerated development and deployment of new analytics workloads.
Analytics assets assessed
Reports, semantic models and dependencies were evaluated as part of the migration program.
Tables and views assessed
The migration factory created visibility into the complete Synapse data estate.
Faster issue resolution
Common monitoring and dependency visibility reduced time spent diagnosing platform issues.
Lower redundant workloads
Assessment identified unused and overlapping assets that could be retired rather than migrated.
The organization moved to a more integrated analytics platform with a clear path for future modernization.
The migration reduced technical debt while giving data and analytics teams a common platform for engineering, warehousing, reporting and self service analytics.
Reduced Migration Risk
Asset classification, dependency mapping and phased cutover reduced the risk of moving critical reporting and analytics workloads.
Better Platform Economics
Workload rationalization and Fabric capacity planning reduced unnecessary platform and operational effort.
Faster Analytics Delivery
Common Fabric patterns shortened the path from new data requirements to production analytics workloads.
Modern Analytics Foundation
The organization gained a unified foundation across data engineering, analytics, semantic modeling and Power BI.
Modernize your analytics platform with a controlled migration.
From Synapse assessment and workload mapping to Fabric architecture, migration, validation and production cutover, a structured migration factory can reduce risk and accelerate the move to Microsoft Fabric.
Discuss your Fabric migration