SAN CARLOS, CALIF. — APRIL 7, 2014 — MarkLogic Corporation, the leading Enterprise NoSQL database platform company, has been included in the Gartner, Inc. Data Warehouse Database Management Systems Magic Quadrant for the first time.
Inclusion in this report, published this month, follows first-time placement for MarkLogic in two other Gartner Magic Quadrants over the last 12 months. MarkLogic appeared in the October 2013 Magic Quadrant for Operational Database Management Systems, and was the only enterprise NoSQL database vendor featured that integrates search and application services. The company was also included in the April 2013 Gartner Magic Quadrant for Enterprise Search report.
“We believe that MarkLogic’s inclusion in these three Gartner Magic Quadrants corresponds with a sustained period of heightened demand for our enterprise-grade NoSQL database platform from multiple industry sectors and on a global scale. Significantly, the Data Warehouse report includes non-relational data management systems for the first time, which we feel is a reflection of the fact that many organizations are architecting their datacenter infrastructures with Enterprise NoSQL to minimize data complexity and capitalize on countless Big Data opportunities in the enterprise,” said Gary Bloom, president and CEO, MarkLogic Corporation.
Major companies across a variety of industries and around the globe are avidly adopting the MarkLogic® Enterprise NoSQL database platform including BBC, Dow Jones, McGraw-Hill Financial, Press Association, Federal Aviation Administration (FAA), Centers for Medicare and Medicaid Services (CMS), National Archives and Records Administration (NARA) and U.S. Department of Agriculture (USDA). Six of the top ten global banks rely on MarkLogic for transactional operations.
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The MarkLogic semantic data platform gives Global 2000 and public sector organizations a faster, trusted way to unlock value from complex data and achieve data agility. The unified platform lets organizations securely connect data and metadata, create and interpret meaning, and consume high-quality contextualized data across the enterprise.