Cloud Migration to Azure
Migrated a legacy on-premise data warehouse to Azure, reducing infrastructure costs by 60% and improving query performance 3× — with zero data loss.
The Challenge
A mid-size logistics company was running an on-premise SQL Server data warehouse on hardware that was 7 years old and approaching end-of-life. Maintenance costs were high, query performance had degraded, and the IT team spent 30% of their time managing infrastructure rather than building value.
What We Built
We designed and executed a full lift-and-migrate to Azure: SQL Server migrated to Azure SQL Managed Instance, ETL jobs moved to Azure Data Factory pipelines, and the reporting layer rebuilt on Azure Synapse Analytics for OLAP workloads. The migration was phased over 6 weeks with a parallel-run period to validate data integrity before cutover.
How It Works
The company's on-premise warehouse had served them well for years, but they were paying for hardware refresh, DBA time, cooling, and power — for infrastructure that sat at 30% utilisation on average. A cloud migration was clearly the right move; the question was how to do it without disrupting daily operations.
We started with a four-week assessment: schema analysis, ETL job inventory, dependency mapping, and workload characterisation. This let us choose the right Azure targets — Azure SQL MI for OLTP workloads, Synapse Analytics for the heavy aggregation queries, and Blob Storage for archive data.
The migration itself ran in parallel. We set up the Azure environment, replicated the on-premise database using Azure Database Migration Service with continuous sync, and let both run in parallel for two weeks. During this time, the team ran queries against both systems and compared results row-by-row on a nightly basis.
Data Factory replaced the legacy SSIS packages for ETL. We rewrote 14 ETL pipelines in Data Factory with proper monitoring, retry logic, and Slack alerting — something the old SSIS setup lacked entirely.
Cutover weekend was smooth: final sync, DNS flip, validation. The team was on the Azure environment by Monday morning. Query times dropped immediately — Synapse's distributed query engine handles the analytical workloads the old SQL Server struggled with. The client now pays 60% less for infrastructure and has autoscaling for peak periods.
