Description
- Databricks features basics to advanced
- Unified analytics platform combining data engineering, data science, and BI in one workspace.
- Interactive notebooks for collaborative development with native Apache Spark execution.
- Managed Spark clusters with autoscaling and optimized runtimes for performance and cost control.
- Delta Lake transactional storage layer providing ACID guarantees, time travel, and schema enforcement.
- Lakehouse architecture that unifies data lake flexibility with data warehouse performance.
- Databricks SQL for low-latency analytics, dashboards, and BI connectivity to semantic models.
- Jobs and orchestration to schedule notebooks, JARs, and Python scripts with dependency management.
- Delta Live Tables for declarative, production-grade ETL with built‑in quality checks.
- Unity Catalog for centralized metadata, fine‑grained access control, and data lineage.
- MLflow integration for experiment tracking, model packaging, and reproducible model deployment.
- Feature stores and model serving to operationalize ML features and provide low‑latency inference.
- Streaming support with Structured Streaming and connectors for Event Hubs, Kafka, and cloud sources.
- Security and compliance features including RBAC, encryption, and enterprise governance controls.
- Performance tuning tools such as query profiling, caching, and adaptive execution for large workloads.
- Collaboration and CI/CD via repos, notebooks versioning, and integration with Azure DevOps/GitHub.
- Advanced capabilities: multimodal ML, large model fine‑tuning, and lakehouse optimizations for scale.
