Enterprise Data Platform Migration to AWS
End-to-end migration of a legacy on-premises data warehouse and analytics stack to a cloud-native architecture on AWS, enabling real-time reporting and reducing total cost of ownership.
Industry
Financial Services
Duration
14 months
Engagement model
Platform modernization and cloud migration program spanning data architecture, delivery orchestration, and production cutover planning.
<5 min
reporting latency
down from 24 hours
40%
lower total cost of ownership
0
production outages during cutover
An aging on-premises data warehouse could not keep pace with growing data volumes and real-time reporting demands. The existing ETL pipeline was brittle, manually maintained, and created a 24-hour reporting lag that impacted trading and risk decisions.
Client context: A financial services organization needed to modernize reporting and analytics without disrupting regulated production workloads or trading visibility.
We designed and executed a phased migration using the AWS Migration Acceleration Program. The new architecture uses Amazon Redshift Serverless for warehousing, AWS Glue for managed ETL, and a real-time streaming layer with Kinesis. Infrastructure was fully codified with Terraform.
Assessment
Mapped legacy data flows, business-critical reports, and migration sequencing requirements.
Target architecture
Designed Redshift, Glue, and Kinesis-based ingestion and warehousing with infrastructure codified in Terraform.
Migration waves
Moved pipelines in stages with validation checkpoints and parallel-run support for reporting teams.
Cutover
Executed a controlled transition with observability, runbooks, and rollback readiness.
Reporting latency reduced from 24 hours to under 5 minutes
40% reduction in total cost of ownership
Zero-downtime cutover for all production workloads
Fully automated infrastructure provisioning
"Devdox gave us a migration path that satisfied the risk committee and the engineering teams at the same time."
Director of Data Platforms
Financial Services
Architecture
- Amazon Redshift Serverless for analytics workloads
- AWS Glue-managed ETL orchestration
- Kinesis streaming for near-real-time ingestion
Business Goals
- Reduce reporting latency for trading and risk teams
- Lower infrastructure overhead and manual ETL maintenance
- Create a scalable analytics foundation on AWS
Constraints
- Zero-downtime transition for production reporting
- Regulated environment with audit and access controls
- Legacy ETL dependencies distributed across multiple teams
Tech Stack