Databricks

Corporate brand

Databricks is a private enterprise software company that provides a cloud-based platform for data engineering, analytics, governance, machine learning, and generative AI.

Last updated November 4, 2026

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Overview

Databricks is an enterprise software company focused on data, analytics, and artificial intelligence. Established in 2013 by members of the team associated with Apache Spark, the company developed a commercial platform intended to make large-scale data processing and machine learning more accessible to organizations. Its central product concept is the lakehouse: an architecture that combines the flexible, comparatively low-cost storage model of a data lake with the management, performance, and analytical capabilities traditionally associated with a data warehouse. The Databricks platform is delivered primarily as a managed cloud service and is designed to operate with major public-cloud environments. It brings together data ingestion and transformation, distributed processing, SQL analytics, business intelligence support, data governance, machine learning workflows, and artificial-intelligence development. This integrated approach is intended to reduce the fragmentation that can arise when companies use separate systems for data lakes, warehouses, notebooks, model development, and production AI applications. A major part of the company's technology ecosystem is Delta Lake, an open-source storage layer that adds transaction management, schema enforcement, versioning, and other reliability features to data-lake environments. Databricks also supports Apache Spark workloads and is closely associated with MLflow, an open-source project for managing machine-learning experiments, models, and deployment workflows. These technologies have helped position the company across both data engineering and data science communities. Databricks has expanded its proposition from data processing toward a broader Data and AI Platform. Its capabilities cover extract, transform, and load processes; lakehouse and SQL warehousing; data sharing; governance and access controls; machine-learning lifecycle management; large-language-model development; and generative-AI applications. The company markets these capabilities to organizations in sectors such as financial services, healthcare, retail, manufacturing, public-sector services, and technology. The brand's positioning emphasizes a data-centric foundation for AI. In practice, this means helping customers organize, govern, and use proprietary enterprise data while building analytical models and AI applications on the same underlying platform. Databricks remains privately held and operates internationally. Its precise ownership structure, financial performance, and executive roster are not specified in the supplied reference material.

History

Databricks was founded in 2013 by researchers and engineers associated with the development of Apache Spark, a distributed computing framework that originated from research at the University of California, Berkeley. The company's early purpose was to commercialize tools and services around large-scale data processing and to help organizations use Spark without having to build and operate the surrounding infrastructure themselves. The company subsequently developed a broader cloud platform for data engineering, analytics, and machine learning. A central element of its strategy was the lakehouse architecture, which sought to combine the economical and flexible storage characteristics of data lakes with the reliability, governance, and query capabilities of data warehouses. This concept became the organizing principle for Databricks' platform and its market identity. Databricks' ecosystem has included major open-source projects. Delta Lake was developed as a storage layer intended to improve reliability and transactional behavior in data lakes. MLflow was created to address recurring problems in machine-learning lifecycle management, including experiment tracking, model packaging, registry functions, and deployment. Apache Spark, Delta Lake, and MLflow together helped establish the company in both data-platform and machine-learning communities. Over time, Databricks broadened its offering beyond distributed processing and notebooks. The platform came to include managed data pipelines, SQL-based analytics, cloud data warehousing, governance, cataloging, data sharing, machine-learning operations, and tools for developing generative-AI systems. The company now presents these capabilities as an integrated Data and AI Platform, with the stated goal of allowing customers to manage data and build analytical or AI applications within a common environment. Databricks serves enterprise and institutional customers across multiple industries and regions. Its current positioning focuses on the relationship between governed organizational data and AI quality, arguing that a dependable data foundation is essential for analytics, machine learning, and generative-AI applications. The company is privately held. The supplied material does not establish detailed dates for individual product releases, financing events, acquisitions, leadership changes, or financial results, so those areas are not expanded here.

2013
Databricks founded
[1]Databricks official websitedatabricks.com
Databricks was established by members of the team associated with Apache Spark to commercialize large-scale data-processing technology and related cloud services.
Expansion into the lakehouse model
[1]Databricks official websitedatabricks.com
The company developed and promoted the lakehouse architecture, combining data-lake flexibility with data-warehouse-style management and analytics.
Broadening into a Data and AI Platform
[1]Databricks official websitedatabricks.com
Databricks expanded its platform from data engineering and Spark workloads into governance, SQL analytics, machine learning, data sharing, and generative AI.

Products and positioning

A unified cloud platform for enterprise data, analytics, machine learning, governance, and AI, centered on the lakehouse architecture and a data-centric approach to AI development.

Enterprise data and AI platform
Databricks Data Intelligence Platform
The company's overarching cloud platform for bringing together data engineering, analytics, governance, machine learning, and artificial-intelligence development. It is designed to provide a common environment in which organizations can prepare data, query it, manage access, develop models, and build AI applications.
Cloud data platform
Lakehouse Platform
Databricks' lakehouse offering is built around the idea that a single architecture can support both large-scale data-lake storage and warehouse-style analytical workloads. It is intended to reduce duplication between separate data infrastructure systems while supporting engineering, BI, data science, and AI use cases.
Open-source data storage layer
Delta Lake
Delta Lake is an open-source storage layer for data lakes. It adds features such as transaction handling, schema management, versioned data, and reliability controls, helping organizations operate analytical data with characteristics commonly expected from managed warehouse systems.
Open-source machine-learning lifecycle platform
MLflow
MLflow is an open-source project associated with Databricks that supports machine-learning development and operations. Its capabilities address experiment tracking, model packaging, model management, and deployment-related workflows, helping teams organize the path from experimentation to production.
SQL analytics and data warehousing
Databricks SQL
Databricks SQL provides SQL-oriented access to lakehouse data for analytics and business-intelligence workloads. It extends the platform beyond engineering and notebook-based work so that analysts and reporting users can query and explore data through warehouse-style interfaces.
Data governance and cataloging
Unity Catalog
Unity Catalog is Databricks' governance layer for organizing, discovering, securing, and managing data and other analytical assets. It supports centralized oversight of access and metadata across data, analytics, machine-learning, and AI workflows.

Recent events

2026.10.12
Reported2026.10.15
PROS launches Kairos, an AI-powered dynamic offer platform for airlines[2]PROS推出面向航空公司的Kairos AI动态报价平台travelerstoday.com
PROS officially launched PROS Kairos on October 12, 2026, a B2B AI-powered platform that lets airline revenue managers and commercial teams build and adjust fares, ancillary offers, and inventory in real time. The initial release includes two headline features, Product Catalog and Stock Keeper, both of which require airlines to sign new commercial agreements before access, and the platform uses Databricks as its underlying data and AI platform.
Unconfirmed
Reported2026.09.25
Cal sells football stadium naming rights to Databricks[3]加州大学伯克利分校将橄榄球场冠名权出售给Databricksnews.google.com
Berkeleyside reports that Cal (UC Berkeley) has sold the naming rights to its football stadium to AI company Databricks.
2026.09.24
Reported2026.09.24
Databricks acquires cloud spreadsheet startup Row Zero[4]Databricks 收购云电子表格初创公司 Row Zerotechcrunch.com
Databricks announced on Thursday that it has acquired early-stage cloud spreadsheet startup Row Zero, which Databricks' finance team had used as a tool able to scale beyond 1 million live spreadsheet rows and had combined with Databricks' AI agent Genie.
Weekdays and relative dates follow the original report; their exact calendar dates have not been verified.
Unconfirmed
Reported2026.08.13
Databricks acquires Electric to bring local Postgres databases to agentic apps[5]Databricks 收购 Electric，将本地 Postgres 数据库引入智能体应用infoworld.com
Databricks is acquiring Electric, a startup that brings WebAssembly-based Postgres databases into application environments, for an undisclosed sum, to let developers run data closer to AI agents. Electric's PGLite and Electric Sync will complement Databricks' Lakebase Postgres database.
Unconfirmed
Databricks launches Partner Connect[6]Databricks推出Partner Connect服务ithome.com.tw
Databricks introduced Partner Connect, a service that connects platform users with partner services and third-party data and tools through API integrations.
Unconfirmed
Databricks Serverless SQL enters public preview on AWS[7]Databricks Serverless SQL在AWS推出公开预览ithome.com.tw
Databricks Serverless SQL is available as a public preview on AWS, and interested users can apply to try it.
2021.05.31
Reported2021.05.31
Qlik and Databricks expand strategic partnership[8]Qlik与Databricks扩大战略合作technuter.com
Qlik announced an expansion of its strategic partnership with Databricks, adding support for Delta Sharing, Databricks’ open protocol for secure data sharing.
2021.05.26
Reported2021.05.26
Databricks launches Delta Sharing[9]Databricks 推出 Delta Sharingprnewswire.com
Databricks launched Delta Sharing, an open-source protocol for securely sharing data across organizations in real time. The project was announced at the Data + AI Summit and is being donated to the Linux Foundation.
2021.05.24
Reported2021.05.24
Rivery announces partnership with Databricks[10]Rivery宣布与Databricks建立合作伙伴关系prnewswire.com
Rivery announced a partnership with Databricks that enables Databricks customers to use Rivery to orchestrate data ingestion pipelines, run transformation processes, and return production machine-learning data sets to business applications.
2021.05.04
Reported2021.05.04
Hivemind and Databricks announce partnership[11]Hivemind与Databricks宣布合作finanznachrichten.de
Hivemind announced a partnership with Databricks to combine their platforms for data preparation, analytics, and related enterprise data-science workflows.

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