SNOWFLAKE MIGRATION

PROJECT DESCRIPTION

The client needed to modernize their data platform and reduce ongoing infrastructure costs. Their Databricks-based setup had become expensive to maintain and increasingly difficult to scale. We delivered a phased migration to Snowflake, building a more centralized and cost-efficient foundation for analytics and machine learning.

CLIENT BACKGROUND

The company relied heavily on data to support multiple business units, with pipelines pulling information from several systems. As reporting needs grew, so did the complexity of the architecture, making it harder to maintain performance, consistency, and governance across teams.

BUSINESS CHALLENGE

While dashboards and analytics were in place, the underlying data ecosystem was fragmented and costly. The client needed a single source of truth that could support near real-time ingestion, structured transformations, stronger governance, and ML-ready datasets, without requiring a heavy operational overhead or enterprise-only infrastructure.

TECHNOLOGY STACK

Snowflake, AWS Step Functions, Snowpipe Streaming, AWS DMS

VALUE DELIVERED

We implemented Snowflake as a scalable SSOT and optimized loading and orchestration to significantly reduce platform costs. The new architecture improved reliability and auditability through better governance and structured raw data handling. Snowflake usage was optimized with up to 70% cost savings, while the platform became fully prepared for advanced analytics and machine learning workflows.

OPTIMIZING LIVE CONNECTION PERFORMANCE

TABLEAU REPORT RECOVERY

DOSE-CONTROLLED MEDICATION DELIVERY