Databricks is a unified data analytics and artificial intelligence platform built around the lakehouse architecture, which combines the capabilities of data lakes and data warehouses into a single platform for data engineering, data science, machine learning, and business analytics. Founded in 2013 by the original creators of Apache Spark at UC Berkeley, including Ali Ghodsi, Matei Zaharia, and five other co-founders, Databricks is headquartered in San Francisco, California. The platform is built on and extends Apache Spark, providing a managed cloud environment for processing massive datasets and building AI applications. Databricks offers several integrated components. The Unity Catalog provides unified data governance across all data and AI assets. Delta Lake, an open-source storage layer, provides ACID transactions, schema enforcement, and time travel for data lakes. MLflow, another Databricks-originated open-source project, provides experiment tracking, model registry, model serving, and ML lifecycle management. Databricks SQL enables SQL analytics and dashboarding directly on lakehouse data. The platform includes Mosaic AI, its suite of AI and machine learning tools that encompasses model training, fine-tuning, serving, and monitoring. Mosaic AI Agent Framework supports building compound AI systems and retrieval-augmented generation applications. Databricks also offers Foundation Model APIs for accessing popular large language models and Vector Search for similarity search on embeddings. The platform runs on all major cloud providers including AWS, Azure, and Google Cloud, with customers deploying within their own cloud accounts for data security and compliance. Databricks pricing follows a consumption-based model using Databricks Units (DBUs), with rates varying by workload type and compute tier. The platform serves organizations of all sizes, from startups to the largest enterprises in the world, across industries including financial services, healthcare, retail, media, and technology.

AI 分析工具

Databricks SQL 直接在数据湖仓库数据上提供商业智能和分析功能，具有用于自动化洞察生成和自然语言查询的 AI 增强功能。该平台使组织能够在不在系统之间移动数据的情况下，将分析工作负载与数据工程和 ML 工作流一起运行。

AI 数据分析

Databricks 提供统一的平台，在数据湖仓库架构上进行大规模 AI 驱动的数据分析，结合数据工程和分析。该平台支持 SQL 分析、使用 Python 和 R 的基于笔记本的探索，以及通过自然语言界面进行 AI 辅助数据分析，使组织能够从 PB 规模的数据集中获得洞察。

AI MLOps工具

Databricks 集成了广泛采用的开源 MLOps 框架 MLflow，用于实验跟踪、模型版本控制、模型注册表和生产服务。该平台从数据准备到模型部署和监控提供端到端 ML 生命周期管理，通过 Unity Catalog 实现所有 ML 资产的统一治理。

AI模型托管

Databricks 通过 Mosaic AI 提供模型服务，为在生产环境中部署机器学习模型和基础模型提供托管端点。该平台支持实时和批处理推理、自动扩展、A/B 测试和模型监控，以及用于在 Databricks 环境中访问流行 LLM 的基础模型 API。

AI 研究工具

Databricks 通过协作笔记本、用于大规模实验的分布式计算和用于实验跟踪和可重现性的 MLflow 来支持 AI 研究。其 Mosaic AI 研究部门为开源 LLM 开发做出贡献，包括 DBRX 模型，该平台被学术界和工业界的研究团队使用。

AI训练平台

Databricks 提供分布式计算基础设施，使用 Apache Spark 和 GPU 集群进行大规模机器学习模型训练。Mosaic AI 套件支持大规模模型训练、基础模型微调和跨所有主要云提供商的分布式深度学习工作负载，具有自动扩展的计算资源。