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Data Engineering

Enterprise Data Warehouse Modernization

Migrated legacy data warehouse to a modern cloud-based architecture, enabling real-time analytics across 20+ business units.

Data WarehouseCloudETL

The Challenge

The client operated a decade-old on-premise data warehouse that could no longer keep pace with growing data volumes. Query times had ballooned to over 15 minutes for complex reports, and the ETL pipelines required constant manual intervention. Business units were making decisions on stale data, sometimes 48 hours old.

Our Solution

We designed and executed a phased migration to a cloud-native data warehouse on Snowflake, rebuilding ETL pipelines with Apache Airflow for full orchestration and monitoring. A semantic layer was introduced so analysts could self-serve without writing SQL. The migration was completed with zero business disruption over a 4-month rollout.

Results

Measurable Impact

60%

Faster query performance

20+

Business units connected

< 5 min

Data freshness (from 48h)

Zero

Downtime during migration

Tech Stack

Technologies Used

SnowflakeApache AirflowPythondbtAWS S3Terraform

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