Case Study: Explorium automates ELT pipeline building and accelerates product development with Databricks

Fueling growth with predictive models and improved customer experience

Explorium, a data platform that helps organizations find the right data and build predictive models, was challenged by ELT pipeline complexity and data latency. Its data engineers were spending too much time manually building pipelines in Spark, Scala, and PySpark, which slowed the release of new data products and customer-facing data sources.

To solve this, Explorium implemented the Databricks Data Intelligence Platform with dbt, along with Databricks Lakeflow Jobs and Auto Loader. With Databricks, Explorium automated its medallion-architecture workflows, enabled analysts and product developers to build and test pipelines without complex coding, and freed engineers to focus on infrastructure. The company says its product development pipeline now moves 10 times faster, dramatically reducing time to deliver new data to the platform and customers.

Explorium

Anton Peniaziev

Data Engineer and Tech Lead