Modernizing Azure Synapse architecture unlocked enterprise analytics velocity.
Pythian optimized Azure Synapse to eliminate resource contention and establish Microsoft Fabric readiness.
The global manufacturing enterprise was ready to empower its data teams and scale telemetry insights across 11 countries, but chronic platform instability and ten-minute dashboard delays stalled daily reporting. Leaning into Pythian, the enterprise mapped out a strategy to eliminate deep technical debt and isolate conflicting workloads within its existing Azure Synapse estate. Pythian restructured dedicated SQL pools and established a structured data foundation on Azure Data Lake Storage Gen2 (ADLS Gen2). Instead of spending their mornings troubleshooting failed data feeds, internal engineers now focus entirely on launching high-value predictive manufacturing machine learning models.
Pythian eliminated our chronic resource contention issues overnight. Our global production dashboards now populate in seconds instead of minutes, allowing our operations teams to make real-time supply chain decisions without worrying about platform stability."
Director of Data Engineering
Global Manufacturing Enterprise
$1.4M
Estimated annual Azure savings
45%
Query performance improvement
99%
System uptime post-engagement
Eliminate performance bottlenecks and prevent runaway infrastructure costs.
The global manufacturing enterprise confronted plant telemetry scale mismatches and critical shipping data delivery delays.
Pythian empowered the internal data team to shift from daily platform maintenance to proactive machine learning development.
Our data engineers were buried under daily operational fire drills patching failed pipelines. Pythian returned hours of deep engineering time to our staff every day, enabling us to shift from system maintenance to building core machine learning models."
Director of Data Engineering
Global Manufacturing Enterprise
Massively over-provisioned SQL pools ran around the clock
Unoptimized Dedicated SQL Pools consumed maximum data warehouse units continuously, regardless of actual workload demands.
Supply chain tracking reports took ten minutes to load
Resource contention between business units forced executive dashboards to buffer endlessly during peak operational hours.
Broken data feeds went undetected until final reporting
The lack of proactive alerting meant data engineers only discovered broken Azure Data Factory pipelines after business users found discrepancies.
Technical debt stalling modern Microsoft Fabric migration
With 200 legacy SQL Server Integration Services packages embedded in operations, the team lacked the roadmap required to transition safely to a modern Lakehouse architecture.
Workload isolation and pool right-sizing stopped immediate platform bleeding.
Pythian separated reporting workloads from large data processing tasks to prevent system contention and eliminate daily performance issues. By adjusting index patterns, data distribution paths, and query views, the engineering team optimized the data platform layout. This technical fix safely reduced standard data delivery cycles from 10 minutes to under three seconds while ensuring resource use matched operational requirements.
Automated pause routines and robust pipeline monitoring eliminated hidden errors.
Implementing automated Auto-Pause and Auto-Resume routines for dedicated SQL pools stopped idle resource waste during off-peak hours. Drawing on experience across 100,000 managed workloads, the team resolved data warehouse unit (DWU) inefficiencies and serverless partition scanning issues. Proactive alerting and automatic retries replaced silent Azure Data Factory pipeline failures to ensure data feed integrity.
Microsoft Purview integration enforced data security and unblocked AI development.
Integrating Microsoft Purview allowed the team to establish enterprise-grade data classification, role-based access control, and dynamic data masking across storage layers. This comprehensive governance framework ensured complete regulatory compliance and successfully unblocked the client's stalled predictive-maintenance AI pilot.
Deployed Medallion storage layout created a clear roadmap for future platform migration.
Pythian structured the cloud storage layer into refined data zones to prepare the business for an eventual transition into Microsoft Fabric. Engineering teams mapped out 200 legacy operational packages and pipeline dependencies to evaluate migration viability. Through this deep analysis, Pythian identified that 60% of the existing workloads could be automatically moved into Microsoft OneLake, preventing manual refactoring delays.
Ongoing 24/7 managed services shifted internal engineering from reactive firefighting to proactive platform innovation.
The project transitioned seamlessly into Pythian's ongoing managed services model to safeguard optimization long after initial deployment. Continuous 24/7 monitoring of Synapse Link, active pipelines, and system security posture freed up internal teams to focus on core technical business expansion.
Accelerate your data platform transformation and establish future migration readiness.
Harnessing the full power of the Microsoft Ecosystem
As a trusted Gold Microsoft Partner, Pythian integrates cutting-edge Azure Synapse and Microsoft Fabric capabilities to solve complex enterprise-scale challenges.
Our deep engineering expertise ensures your legacy data workloads mesh seamlessly with modern cloud architecture. Discover how our tailored data warehouse consulting services can stabilize your environments, optimize cloud spend, and prepare your infrastructure for next-generation AI initiatives.
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