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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. This philosophy is part of everything we do to advance and execute on lakehouse. 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.Ī commitment to open source, open standards, open communityĭata lakehouse architecture is inherently open, built on a vision of unifying your data ecosystem without proprietary restrictions. 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.
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Both developers and business users have real-time access to analytics via Databricks SQL and PowerBI, and time-to-market has decreased by 60%. 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. 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.With more than 5,000 global customers, we’re humbled and inspired by the amazing problems our customers have tackled with lakehouse architecture. Our platform combines the data management and performance typically found in data warehouses with the low-cost, flexible object storage offered by data lakes. We built the Databricks Lakehouse Platform to tackle the most pressing, complex challenges around enterprise data. The shift to data lakehouse architecture has become increasingly prevalent as customers’ needs across analytics and AI become too complicated for their existing architectures. 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 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.
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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. The promise of lakehouse architecture is delivered.
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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. It’s a piecemeal approach to problem solving that checks boxes, but doesn’t create a simple or unified experience for customers. But, they are assessed on disparate products in their portfolio that individually accomplish the criteria of the report. Other vendors show up in multiple Magic Quadrants each year across many domains. We feel the true achievement here is not in the placement, but instead in how it was accomplished. 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.Ī complimentary copy of the report can be downloaded here. Today, we are thrilled to announce that Databricks has been named a Leader in 2021 Gartner® Magic Quadrant for Cloud Database Management Systems.