
Databricks is the pioneer of the Data Lakehouse architecture, a unified platform that combines the performance and governance of data warehouses with the flexibility and scalability of data lakes. Built on open-source foundations including Apache Spark, Delta Lake, and MLflow, Databricks provides a collaborative environment for data engineers, data scientists, and analysts. In 2026, the platform centers on Mosaic AI, offering end-to-end tooling for building, deploying, and monitoring compound AI systems and Large Language Models (LLMs). The technical core features the Photon engine for high-performance vectorized execution and Unity Catalog for unified governance across data and AI assets. Databricks' strategy focuses on 'Data Intelligence,' using generative AI to simplify data management and democratize insights. Its serverless compute options have matured to provide near-instant cold starts, significantly reducing operational overhead for SQL workloads and model serving. By integrating vector databases directly into the Lakehouse, Databricks facilitates seamless Retrieval-Augmented Generation (RAG) workflows, making it a critical infrastructure component for enterprises scaling private AI applications.
A unified governance layer for all data and AI assets including files, tables, and machine learning models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Scalable training of private LLMs from scratch or fine-tuning existing models on proprietary data.
A high-performance C++ vectorized execution engine that accelerates Spark workloads.
A declarative framework for building reliable, maintainable, and testable data processing pipelines.
AI-driven monitoring of data quality and model performance without extra infrastructure.
Instant compute for SQL queries that removes the need for cluster management.
Integrated vector database that automatically synchronizes with Delta tables.
Securely providing LLMs with private enterprise data for context-aware responses.
Registry Updated:2/7/2026
Monitor quality with MLflow.
Identifying fraudulent transactions with sub-second latency.
Reducing downtime by predicting equipment failure using IoT sensor data.