Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. It was founded in 2013 by the original creators of Apache Spark, Delta Lake, and MLflow. Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI. Some key features of Databricks include:

• Data processing: Databricks provides a unified interface and tools for most data tasks, including data processing workflows scheduling and management, working in SQL, generating dashboards and visualizations, data ingestion, managing security, governance, and HA/DR, data discovery, annotation, and exploration, compute management, and machine learning (ML) modeling and tracking.

• Lakehouse: Databricks combines data warehouses and data lakes into a lakehouse architecture, which unifies all data, analytics, and AI workloads using one platform.

• Machine learning: Databricks machine learning expands the core functionality of the platform with a suite of tools tailored to the needs of data scientists and ML engineers, including high-quality, highly performant data pipelines, and the ability to accelerate ML across the entire lifecycle, from featurization to production.

• Data governance: Databricks provides a single model of data governance for all structured and unstructured data, which helps maintain a compliant, end-to-end view of your data estate.

Databricks is a cloud-based data engineering tool that is widely used by companies to process and transform large quantities of data. It is used by more than 9,000 organizations worldwide, including ABN AMRO, Condé Nast, Regeneron, and Shell.