Databricks
A unified data intelligence platform combining Apache Spark-based data engineering, Delta Lake storage, ML experimentation, and SQL analytics on a lakehouse architecture across AWS, Azure, and GCP.
How RevOps teams use it
RevOps and data engineering teams use Databricks for large-scale revenue data transformation, feature engineering for ML models (churn prediction, lead scoring), and building a lakehouse that serves both analytics and machine learning workloads.
Example use case
e.g., A RevOps data scientist uses Databricks to train a churn prediction model on 3 years of CRM, product usage, and support data — using Delta Lake to version features and MLflow to track experiments — deploying the final model as a Snowflake UDF.
Where it's used
Technical Disciplines
ML Engineer
Builds machine learning models that power revenue predictions — from churn scoring and lead ranking to pipeline forecasting and pricing optimisation.
RevOps Data Engineer
Designs and maintains the data infrastructure — pipelines, warehouses, and integrations — that ensures revenue data is accurate, available, and actionable.