We’re excited to announce that for the fourth consecutive time, Gartner has acknowledged Databricks as a Chief within the 2025 Gartner® Magic Quadrant™ for Information Science and Machine Studying Platforms. Databricks has acquired the best place in Means to Execute and the furthest place in Completeness of Imaginative and prescient.
Gartner defines a knowledge science and machine studying platform as an built-in set of code-based libraries and low-code tooling. These platforms assist the impartial use and collaboration amongst information scientists and their enterprise and IT counterparts, with automation and AI help by all phases of the information science life cycle, together with enterprise understanding, information entry and preparation, mannequin creation and sharing of insights. In addition they assist engineering workflows, together with the creation of knowledge, characteristic, deployment and testing pipelines. The platforms are supplied through desktop consumer or browser with supporting compute cases or as a completely managed cloud providing.
Obtain a complimentary copy of the report right here.
We’re thrilled about this recognition from Gartner and imagine it’s because of the success of the hundreds of Databricks clients who’ve constructed and deployed high-quality AI initiatives into manufacturing. For a few years, enterprises have struggled to place their information science and machine studying initiatives into manufacturing. GenAI has solely made it harder as a result of AI basis fashions will not be conscious of enterprise information and fail to ship business-specific, correct, and well-governed outputs.
At Databricks, our focus has been to assist enterprises construct and deploy AI in high-value, mission-critical purposes whereas making certain accuracy, governance, and ease of use. Our innovation pillars are:
- AI Brokers that cause over your information: Databricks supplies probably the most environment friendly and safe solution to join your enterprise information to brokers. With the AI platform constructed on the lakehouse, there isn’t a have to duplicate information. This makes it straightforward to customise AI fashions together with your information.
- Customized analysis in your use case: Databricks presents a built-in analysis for brokers. You possibly can consider and use any mixture of open supply and industrial GenAI fashions, in addition to ML fashions in your AI Brokers. We enable you measure the output high quality of the brokers and provide you with strong methods to hint the basis trigger, consider fixes, and redeploy rapidly to enhance high quality.
- Unified governance throughout information, AI fashions, and instruments: Prospects can govern and apply guardrails throughout all AI fashions, together with these hosted exterior of Databricks. We robotically implement correct entry controls, set price limits to handle prices, forestall dangerous content material, and monitor lineage all through your complete AI workflow from information to fashions.
Databricks on Databricks
At Databricks, we’re massive proponents of utilizing our personal expertise internally. Apparently, the instruments being evaluated on this Magic Quadrant report had been the instruments we leveraged to finish our Magic Quadrant questionnaire. Anybody who has labored on a Magic Quadrant is aware of that the questionnaires are extremely rigorous and require ample time from stakeholders throughout the corporate. Leveraging the Databricks Information Intelligence Platform, we constructed our personal customized information base AI agent named ARIA – Analyst Relations Clever Assistant – to write down high-quality and high-accuracy first drafts for almost 700 pages price of technical product questions. This saved the staff tens of collective hours of writing time and enabled our management staff to deal with extra high-value, strategic parts of the analysis.
ARIA is constructed on a Retrieval-Augmented Technology (RAG) structure, wrapped in a user-friendly Streamlit interface and hosted on Databricks Apps. It ingests RFI paperwork in HTML format, extracts questions, and generates high-quality responses utilizing Mosaic AI Agent Framework, Vector Search, and batch inference with Claude 3.7-Sonnet. The system leverages prior Q&A pairs, Databricks documentation, and a product-to-keyword mapping desk to boost search relevance. DSPy is used for immediate optimization to make sure consistency in tone and format, and the ultimate output is exportable to Google Docs or Excel for collaboration.
What’s subsequent
We imagine our recognition as a Chief with the best scores for Means to Execute and Completeness of Imaginative and prescient is a testomony to our capability to convey collectively groups and allow them to create the following technology of knowledge and AI purposes with high quality, pace, and agility.
As a pacesetter throughout a number of Magic Quadrants and different analyst reviews, we imagine the distinctiveness of the achievement is in the way it was achieved. It’s not unusual for distributors to point out up in a number of Magic Quadrants every year throughout many domains. However, they’re assessed on disparate merchandise of their portfolio that individually accomplish the particular standards of the report. Databricks’ outcomes present definitively you could be a pacesetter with a unified strategy to Information + AI, with one copy of knowledge, one processing engine, one strategy to administration and governance that’s constructed on open supply and open requirements throughout all clouds.
With a single answer, you possibly can ship class-leading outcomes for information warehousing and information science/machine studying workloads. We imagine that ML and GenAI will proceed to remodel information platforms, and we thank our clients and companions for becoming a member of us on this journey.
Be taught extra
To study extra about Mosaic AI, go to our web site and observe @Databricks for the most recent information and updates. You can too be part of us on the Information + AI Summit 2025, the place we are going to make vital bulletins throughout our innovation pillars for AI.
Learn the Gartner Magic Quadrant for Information Science and Machine Studying Platforms.
Gartner, Magic Quadrant for Information Science and Machine Studying Platforms, Afraz Jaffri, Maryam Hassanlou, Tong Zhang, Deepak Seth, Yogesh Bhatt, Might 28 2025.
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