Databricks

About Databricks

Cloud data platform supporting data engineering, collaborative data science, machine learning, and analytics. Built on Apache Spark with Delta Lake for reliable data lakes. Ideal for organizations focusing on advanced analytics, ML workflows, and collaborative data science with notebooks.

Key Features

• 1Unified lakehouse platform combining data lake storage with warehouse performance
• 2Managed Apache Spark with Delta Lake for ACID transactions on object storage
• 3MLflow integration for experiment tracking, model registry, and serving
• 4Delta Live Tables for declarative, auto-managed streaming and batch pipelines
• 5Unity Catalog provides unified governance across data, ML, and analytics

How Python Data Engineers Use Databricks

Python data engineers use Databricks notebooks and jobs to run PySpark ETL pipelines on Delta Lake. Engineers write Python and SQL in notebooks, schedule jobs via the Databricks Jobs API or Airflow, and use the Databricks Connect client to run Spark commands from local IDEs. MLflow auto-logging captures experiment metrics without any extra code.