Case Studies

Case Study: Modernizing Clinical Trial Forecasting on Databricks | 12x Cost Optimization

Written by Admin | Mar 3, 2026 11:46:28 AM

The Challenge

 

Compute-Heavy Full Loads

ADF full-load pipelines ran for nearly 10 hours, and incremental loads ran for about 45 minutes, driving high Azure resource consumption and increasing platform costs.

 

Pipeline Fragmentation

The team managed six separate ADF pipelines for full and incremental processing, increasing orchestration complexity and maintenance overhead.

 

Hard Delete Reconciliation

The team executed weekly full reloads to address source-side hard deletes, adding unnecessary processing time.

 

Reporting Discrepancies

Business reports diverged from source data until the team ran a full load to realign datasets.

 

Workflow Timeouts

Long-running full loads frequently timed out, forcing production support teams to monitor pipelines continuously and intervene manually.