A logistics company's on-premises ERP database was struggling under shipment and tracking data volumes. Migrating the analytical workload to Azure Synapse Analytics with a redesigned columnar model cut nightly batch time from 8 hours to 45 minutes.
The ERP database handled both transactional and analytical workloads on the same SQL Server instance. Nightly batch jobs that generated shipment reports and KPI calculations were taking 8+ hours, missing SLA windows and delaying morning operations reviews. The business could not scale without a fundamental architectural change.
Used Azure Migrate to profile the ERP SQL Server instance, identifying which queries consumed the most resources and which workloads were analytical vs. transactional — generating an AI-recommended separation strategy.
Redesigned the analytical schema for Azure Synapse Analytics using distributed columnar tables, hash distribution on shipment ID, and clustered columnstore indexes — optimized for the specific query patterns identified in the assessment.
Developed Azure Data Factory pipelines to replicate completed transactions from the on-premises ERP to Azure Synapse in near-real-time, keeping the transactional ERP on-premises while offloading all analytical queries to Synapse.
Rewrote all 23 nightly batch T-SQL jobs to run against Azure Synapse, using Copilot for Azure Data to optimize query patterns for the distributed columnar architecture — replacing cursor-based logic with set-based operations.
Pointed all Power BI reports to Azure Synapse using DirectQuery with aggregations, enabling real-time analytical queries without impacting the transactional ERP performance.
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