Databricks Named a Leader in 2021 Gartner® Magic Quadrant for Cloud Database Management Systems
December 16, 2021 in Platform Blog
Today, we are thrilled to announce that Databricks has been named a Leader in 2021 Gartner® Magic Quadrant for Cloud Database Management Systems. We believe this achievement makes Databricks the only cloud-native vendor to be recognized as a Leader in both the 2021 Magic Quadrant reports: Cloud Database Management Systems and Data Science and Machine Learning Platforms.
A complimentary copy of the report can be downloaded here.
We feel the true achievement here is not in the placement, but instead in how it was accomplished. Other vendors show up in multiple Magic Quadrants each year across many domains. But, they are assessed on disparate products in their portfolio that individually accomplish the criteria of the report. It’s a piecemeal approach to problem solving that checks boxes, but doesn’t create a simple or unified experience for customers. The results across these two reports definitively show that one copy of data, one processing engine, one approach to management and governance that’s built on open source and open standards – across all clouds – can deliver class-leading outcomes for both data warehousing and data science/machine learning workloads. The promise of lakehouse architecture is delivered.
The 2021 Gartner® Magic Quadrant for Cloud Database Management Systems is based on the rigorous evaluation of 20 vendors on both the completeness of vision each vendor sets forth and their ability to execute on it. At Databricks, we’ve been rapidly expanding and advancing our lakehouse platform to enable data teams to drive new data and AI use cases and to unlock the value in all of their data, and we’re very pleased to see that work recognized. While we are just scratching the surface, we believe these are the biggest strengths of the Databricks Lakehouse Platform that contributed to our placement in the Gartner Magic Quadrant:
A simple platform to unify all your data, AI and analytics workloads
The shift to data lakehouse architecture has become increasingly prevalent as customers’ needs across analytics and AI become too complicated for their existing architectures. We built the Databricks Lakehouse Platform to tackle the most pressing, complex challenges around enterprise data. Our platform combines the data management and performance typically found in data warehouses with the low-cost, flexible object storage offered by data lakes.
With more than 5,000 global customers, we’re humbled and inspired by the amazing problems our customers have tackled with lakehouse architecture. Two of our favorite stories are:
- Northwestern Mutual has moved from a legacy data warehousing stack to Databricks Lakehouse to enable 9,300 financial advisors to gain a 360 customer view that helps to personalize interactions with their clients. Databricks is running 300+ ELT jobs that unify millions of data points in different formats, with superior performance, at a lower cost and simplified governance. Both developers and business users have real-time access to analytics via Databricks SQL and PowerBI, and time-to-market has decreased by 60%.
- McDonald’s has accelerated time-to-value with Databricks Lakehouse, leveraging it as an open platform in a multi-cloud environment to deliver ML and BI across the enterprise. In under 9 months, McDonald’s leveraged the platform’s MLOps capabilities to enable faster delivery of production-ready models that support use cases from menu personalization to customer lifetime value, with a roadmap of analytics use cases leveraging Databricks SQL.
A commitment to open source, open standards, open community
Data lakehouse architecture is inherently open, built on a vision of unifying your data ecosystem without proprietary restrictions.
This philosophy is part of everything we do to advance and execute on lakehouse. To date, we’ve launched five open source projects, including Delta Lake (the enabler of lakehouse architecture) and Delta Sharing, an open protocol for secure real-time exchange of large datasets that enables secure data sharing across products for the first time. Additionally, we recently launched Partner Connect, a one-stop portal for customers to quickly discover a broad set of validated data, analytics, and AI tools and easily integrate them with their Databricks lakehouse across multiple cloud providers.
High performance at the most massive scale
Every company says their products and services are highly-performant and operate at enterprise scale. But at Databricks, this core capability of the Lakehouse platform is truly validated by the community and independent benchmarking. Our customers are driving use cases with sometimes petabytes of storage in their systems.
But don’t just take our word for it. Earlier this month, a third-party benchmark found that the Databricks Lakehouse Platform can outperform data warehouses. On the 100TB TPC-DS benchmark report, the gold standard performance benchmark for data warehousing, Databricks SQL, which achieved general availability yesterday, outperformed the previous record by 2.2x and officially set a new world record in performance.
Our placement helps wrap up an unprecedented year at Databricks, which included raising $2.5 billion at a current $38 billion valuation, proven record-breaking performance and the acquisition of 8080 Labs, a German-based low code/no code startup, to expand our citizen data scientist offering. We feel being named a Leader in both Magic Quadrant reports is especially significant within the context of Lakehouse. Our recognition as a Leader in both cloud database and data science/machine learning is a testament to the success of the lakehouse architecture and its ability to bring together data teams across the entire data and AI workflow.
At Databricks, we continue to innovate and push the boundaries of what’s possible once data teams can break down the barriers of collaboration. Lakehouse brings together data leaders and practitioners to execute any data use case – analytics, data science, data engineering, MLops and so much more. Read the Gartner Magic Quadrants for Cloud Database Management Systems and Data Science and Machine Learning Platforms to learn more.
Gartner, “2021 Cloud Database Management Systems,” Henry Cook, Merv Adrian,
Rick Greenwald, Adam Ronthal, Philip Russom,14th December 14, 2021.
Gartner “2021 Magic Quadrant for Data Science and Machine Learning Platforms,”
Peter Krensky, Carlie Idoine, Erick Brethenoux, Pieter den Hamer, Farhan
Choudhary, Afraz Jaffri, Shubhangi Vashisth, March 1, 2021.
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