Customer Case Study: Iterable - Databricks

Iterable

Customer Case Study

Iterable

Iterable is a technology company that provides a growth marketing platform for cross-channel customer engagement, empowering companies to deliver completely seamless, personalized customer experiences.

Vertical Use Case

  • Leveraging data and AI to help companies deliver a more personalized customer experience

Technical Use Case

  • Ingest, ETL
  • Machine learning

The Challenges

Iterable is on a mission to connect people with the products they love. Their growth marketing platform enables marketing teams to build personalized campaigns across millions of customers and answer questions like what is the best time of day, offer, frequency and channel to drive engagement. Their platform ingests billions of customer events and terabytes of data every day to power machine learning applications. As Iterable built out their data workflows, they faced a number of challenges:

  • Massive Volumes of Data: Manually deploying, scaling and troubleshooting Apache Spark Clusters on EMR to process billions of customer events proved to be time consuming.
  • Siloed Workflows: Disjointed machine learning lifecycle stages plus siloed data engineering and data science teams led to a lack of code reuse and inefficient analytics processes.
  • Machine Learning at Scale: Ability to build, train, and deploy ML models in a repeatable and reproducible manner was impossible due to disjointed systems, different programming languages, etc.

The Solution

Databricks has provided Iterable a fully managed analytics platform on AWS that accelerates AI innovation.

  • Fully Managed Platform: A fully managed cloud platform on AWS simplifies operations and delivers superior performance of data pipelines at scale.
  • Automated Infrastructure Management: Simplified cluster management with auto-scaling significantly reduced time spent on data engineering and development.
  • Faster and More Reliable Data Pipelines at Scale: Removed the complexities of building data pipelines that could scale to meet their data needs.
  • Robust Machine Learning Infrastructure: MLflow greatly streamlined their machine learning lifecycle, simplifying model reproducibility and process repeatability.
  • Interactive Workspace: Data scientists can collaborate, share, and track data and insights across various programming languages, fostering an environment of transparency and improving productivity.

The Results

Databricks’ hands-off infrastructure and collaborative platform for data science and engineering has enabled Iterable to innovate faster while significantly reducing the time spent on low value activities.

  • Lower Total Cost of Ownership: Reduced TCO due to more efficient use of compute resources, self-service platform, and improved cross-team productivity — resulting in an estimated operational cost savings of 20-50%.

The total cost of ownership for Databricks is a big factor for us and the operational savings that come from having to stay hands-off on the infrastructure side. It’s truly a self-serve environment where data scientists and ML engineers are able to run their own interactive clusters.

Ankur Mathur, Senior Engineering Manager, Iterable