Case Study: Iterable achieves scalable ML pipelines and 20–50% lower TCO with Databricks

Iterable - Customer Case Study

Iterable is a growth marketing platform that processes billions of customer events and terabytes of data daily to power personalized, cross-channel campaigns. As their data workflows grew, they struggled with time-consuming manual Spark cluster management on EMR, siloed engineering and data science workflows that limited code reuse, and an inability to build, train, and deploy machine learning models consistently at scale.

Databricks delivered a fully managed analytics platform on AWS with automated, auto-scaling infrastructure, MLflow for reproducible model lifecycle management, and an interactive workspace for cross-language collaboration. The result was faster, more reliable data pipelines, reduced time on low-value operations, improved cross-team productivity, and an estimated 20–50% operational cost savings through lower total cost of ownership.

Iterable

Ankur Mathur

Senior Engineering Manager