Data Warehouse

IT Services Company Saves $1.4M in Annual Azure Spend with Synapse Optimization

3 min read

Mar 24, 2026 2:42:15 PM

Pythian Synapse optimization cuts costs, improves query performance, and enables Fabric migration readiness

A global IT services provider built its analytics backbone on Azure Synapse Analytics. However, spiraling costs, resource contention across business units, and Microsoft's pivot toward Fabric left the data team at a crossroads. Pythian stabilized the environment, cut cloud spend by 35%, improved query performance by 60%, and delivered a production-ready roadmap for Microsoft Fabric migration.

45%

Query performance improvement

$1.4M

Estimated annual Azure savings

99%

System uptime post-engagement

Account profile

Industry: Information Technology and Services

Organization scale: Global enterprise, $900M+ revenue, 5,000+ employees across 11 countries

Tech stack:

Azure Synapse Analytics (Dedicated & Serverless SQL Pools)
Apache Spark for Synapse
Azure Data Factory
ADLS Gen2
Microsoft Purview
Power BI
SSIS (legacy)
Microsoft Fabric (target)

A powerful platform buckling under its own weight

The company had invested heavily in Azure Synapse as its unified analytics environment. Three years in, the platform had become a victim of its own success; more teams, more workloads, more assets, more cost, and less control. Leadership needed the data estate to support an upcoming generative AI initiative, but the foundation wasn't ready.

Microsoft's Fabric pivot forces a reckoning

Microsoft's push toward Fabric forced a hard question: stay on Synapse or migrate to a Lakehouse architecture? With 200+ legacy SSIS packages and complex ADF pipelines embedded in daily operations, the company had no clear plan and no room for a misstep.

Over-provisioned pools and silent failures

Dedicated SQL pools were massively over-provisioned. Spark jobs and SQL queries fought for the same data warehouse units, causing resource contention that crashed Monday morning Power BI dashboards. Pipelines failed silently; nobody knew a data feed was broken until a finance director found bad numbers in a quarterly report.

38% cost surge with zero analytical gain

Azure spend had ballooned 38% year-over-year with no increase in analytical output. Executive dashboards took over 10 minutes to load during peak hours, and the data science team couldn't launch an AI pilot because nobody could confirm who had access to what data.

From cost center to competitive advantage

Pythian didn't lead with Fabric. The engagement started where the pain was: the current Synapse environment. The goal was to stabilize what existed, stop the financial bleeding, and then build the bridge to what comes next.

Discovery phase

Pythian conducted a full readiness assessment of the Synapse estate. The team cataloged every dedicated SQL Pool, Spark notebook, serverless endpoint, and ADF pipeline. The audit revealed over-provisioned DWUs running around the clock, serverless queries scanning entire partitions without column pruning, and zero workload isolation between business-critical reports and experimental Spark jobs.

It also mapped every SSIS package and ADF dependency to determine what could migrate automatically to Fabric and what required a rebuild.

Strategic architecture

The solution addressed every layer of the data stack:

Stabilize and optimize

Isolated executive Power BI workloads from batch and Spark jobs to end outages. Right-sized dedicated SQL Pools (DWUs), shifted sporadic workloads to serverless, and automated Auto-Pause/Resume. Tuned columnstore indexes and distribution keys, and implemented result set caching and materialized views to boost throughput. Replaced silent pipeline failures with alerting, retries, and observability.

Fabric migration roadmap

Delivered a stay-and-optimize vs. migrate recommendation based on risk and timelines. Implemented a Medallion (Bronze/Silver/Gold) layer on ADLS Gen2 to prepare for OneLake, identified 60% of pipelines for automatic migration, and scoped refactoring for the remaining 40%.

Implementation roadmap

Phase 1: Assessment and stabilization

Pythian inventoried all SQL Pools, Spark notebooks, and ADF/SSIS pipelines. The team performed root-cause analysis on resource contention and cost overruns, then deployed workload isolation to stop dashboard outages immediately.

Phase 2: Optimization and hardening

The team right-sized DWUs, migrated sporadic workloads to serverless, and deployed Auto-Pause/Resume automation. Pipeline hardening added observability and automated alerting, and Microsoft Purview was integrated for governance and data classification.

Phase 3: Fabric readiness and managed DataOps

Pythian implemented the Medallion architecture on ADLS Gen2 and delivered the Fabric migration roadmap with dependency mapping for all 200+ SSIS packages. The company transitioned to Pythian's 24/7 managed DataOps model, with continuous monitoring of Synapse Link, pipelines, and security posture.

Measurable business impacts across performance, cost, and operations

The engagement produced a data estate that works, one that's stable today and ready for Fabric tomorrow.

45% Faster query response times

Columnstore index and distribution key optimization cut dashboard load times from over 10 minutes to under three. Resource contention incidents fell to zero.

$1.4M in estimated annual savings

Monthly Azure spend dropped 30% through DWU right-sizing, serverless migration, and Auto-Pause automation. The FinOps audit paid for itself within the first billing cycle.

99.9% system uptime

Automated alerting eliminated silent pipeline failures, and Microsoft Purview integration delivered 100% governance coverage, unblocking the AI pilot that had been stalled for two quarters.

24/7 managed DataOps

The data team shifted from reactive firefighting to proactive platform management under Pythian's continuous monitoring model.

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