Lakehouse Platform: Core architecture that combines data lake storage with data warehouse performance and AI tooling. Supports batch and streaming ingestion, ETL, analytics, ML training, and generative AI on a single data foundation.

Lakebase (Transactional Engine): A Postgres-compatible, serverless engine built on the lakehouse to support transactional and operational workloads, extending Databricks beyond analytics into real-time application backends.

Databricks SQL: High-performance SQL engine for BI and reporting directly on lakehouse data, giving warehouse-style performance without moving data into a separate warehouse product.

Unity Catalog: Central governance layer for data, models, and AI assets. Provides fine-grained access control, unified metadata, lineage tracking, and auditability across all workloads—critical for regulated enterprises.

MLflow + Machine Learning: End-to-end ML lifecycle management: experiment tracking, model registry, deployment, and monitoring. Deeply integrated with Databricks compute to streamline enterprise MLOps.

Generative AI & Agents (Agent Bricks): Framework to build, deploy, and manage AI agents that connect to enterprise data and tools. Designed for production deployment of LLM-powered applications with observability and governance.

Lakeflow & No-Code Data Tools: Visual data pipeline and workflow tooling for non-specialists, enabling analysts and operations teams to build data flows without deep engineering expertise.

Delta Lake & Delta Sharing: Open storage format and sharing protocol that support ACID transactions on data lakes and cross-organization data collaboration without copying data.

Data Clean Rooms: Privacy-preserving collaboration environments that allow multiple parties to run analytics or AI workloads over combined datasets while maintaining strict data access controls.