Main Purpose
The main purpose of Databricks is to provide a data lakehouse architecture, which combines the benefits of data lakes and data warehouses, for integration, storage, processing, governance, sharing, analytics, and AI.
Key Features
- Integration, storage, processing, governance, sharing, analytics, and AI in one architecture.
- Support for structured and unstructured data.
- End-to-end view of data lineage and provenance.
- Toolset for Python and SQL, notebooks and IDEs, batch and streaming, and all major cloud providers.
Use Case
- Scalable storage and processing capabilities for modern organizations.
- Establishing a single source of truth.
- Eliminating redundant costs.
- Ensuring data freshness.
- Real-time data processing.
- Data integration and unification.
- Schema evolution without disrupting existing data pipelines.
- Data transformations with speed, scalability, and reliability.
- Data analysis, reporting, and complex analytical queries.
- Machine learning and AI on all data.
- Data versioning, lineage, and governance.
- Data sharing and collaboration across teams.
- Operational analytics and monitoring.
