Snowflake Migration to Reduce Infrastructure Costs
Challenge
Our client relied on a Databricks-based setup that led to high infrastructure costs, fragmented data systems, and lacked readiness for advanced analytics and ML. The platform was not equipped to support machine learning workflows and scale with growing data demands.
Our client relied on a Databricks-based setup that led to high infrastructure costs, fragmented data systems, and lacked readiness for advanced analytics and ML. The platform was not equipped to support machine learning workflows and scale with growing data demands.
Solution
- We developed a phased migration plan centered on Snowflake as a scalable and cost-effective SSOT.
- Optimized Snowflake loading (up to 70% cost savings).
- Replaced Snaplogic with native AWS Step Functions.
- Enabled Snowpipe Streaming from DynamoDB.
- Set up DMS for Oracle CDC.
- Introduced dynamic tables, DBT, and semantic layers.
- Structured raw data handling.
- We developed a phased migration plan centered on Snowflake as a scalable and cost-effective SSOT.
- Optimized Snowflake loading (up to 70% cost savings).
- Replaced Snaplogic with native AWS Step Functions.
- Enabled Snowpipe Streaming from DynamoDB.
- Set up DMS for Oracle CDC.
- Introduced dynamic tables, DBT, and semantic layers.
- Structured raw data handling.
Results
The migration reduced infrastructure overhead, with Snowflake usage optimized by up to 70%. The centralized architecture streamlined access for business units, while governance improvements increased data reliability and auditability. Most importantly, the platform is now equipped to support machine learning workflows and scale with growing data demands.
The migration reduced infrastructure overhead, with Snowflake usage optimized by up to 70%. The centralized architecture streamlined access for business units, while governance improvements increased data reliability and auditability. Most importantly, the platform is now equipped to support machine learning workflows and scale with growing data demands.
