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# Azure Synapse to Databricks migration delivers a 10X gain in data pipeline performance
**URL:** https://test.sigmoid.com/case-studies/azure-synapse-to-databricks-migration-delivers-a-10x-gain-in-data-pipeline-performance/
Date: 2026-01-12
Author: Joha Momin
Post Type: page
Summary: Azure Synapse to Databricks migration delivers a 10X gain in data pipeline performance Moved a global FinTech provider off a fragmented Synapse...Read More...
---
# Azure Synapse to Databricks migration delivers a 10X gain in data pipeline performance
Moved a global FinTech provider off a fragmented Synapse environment to a unified Databricks lakehouse with end-to-end lineage and real-time insights.
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### Business Scenario
The client's Azure Synapse environment underpinned 35 data products across 19 data subjects, serving finance, service and commercial teams that depended on it for daily decisions. Roughly 80% of the underlying data came from Oracle ERP, on premises and cloud, with the remainder from Salesforce and other systems, moving through close to 160 ADF pipelines. All transformation logic lived inside stored procedures in Synapse's dedicated SQL pool, with no separation between compute for ingestion and compute for analysis.
### Sigmoid Solution
Sigmoid migrated the estate in structured, dependency-ordered waves, rebuilding stored procedures natively on Databricks rather than carrying Synapse's design forward. Table-level dependencies were mapped across all 19 data subjects before migrating any code, then sequenced the program into four milestones. Subjects with no upstream dependencies moved first, the interdependent group spanning Product, Customer and Quote moved together, and Service Part and Service Request, which together hold about 70% of all tables, moved once their dependencies were in place. Synapse's staging, transformation, and business-ready layers were rebuilt as Bronze, Silver and Gold layers on Delta Lake, and migration proceeded layer by layer, so the physical Gold tables stayed stable for downstream consumers throughout the cutover.
[Read more](#)
### Business Impact
10X
improvement in data pipeline performance post-migration
60%
reduction in overall platform cost through consolidated storage and autoscaling
Real-time
query resolution delivering instant insights on demand
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