Cloud Data Warehouse Modernization
Regional Healthcare Analytics Provider
Migrated a decade-old on-premise SQL Server data warehouse to a modern cloud-native architecture, unlocking self-service analytics and cutting infrastructure costs by 60%.
A regional healthcare analytics company was running critical reporting workloads on aging on-premise SQL Server infrastructure. The system couldn't handle growing data volumes — queries that once ran in minutes were taking hours, and the maintenance burden consumed two full-time engineers. A hardware refresh quote came back at $800K, prompting leadership to explore cloud migration.
Adopted a phased migration approach using the Strangler Fig pattern, migrating tables and workloads incrementally to eliminate risk while maintaining full business continuity throughout.
Conducted a 3-week discovery phase to catalog 400+ tables, identify dependencies, and classify data sensitivity for HIPAA compliance
Built AWS Glue ETL pipelines to extract, transform, and load historical data into an S3 data lake with partitioned Parquet format
Provisioned Amazon Redshift Serverless as the primary query engine, leveraging automatic scaling for variable analyst workloads
Implemented dbt Cloud for all transformation logic, enabling version-controlled, tested SQL with full lineage tracking
Ran a 6-week dual-write period validating row counts and aggregate metrics against the legacy system before final cutover
Eliminated $800K hardware refresh cost; cloud spend at $12K/month vs. $28K prior operational costs
Average query execution time dropped from 4.2 hours to 18 minutes
Self-service analytics adoption grew from 6 power users to 40+ analysts within 3 months of go-live
Zero data loss or compliance violations during migration of 8 TB of sensitive healthcare data