MARKLOGIC WORLD, NYC —June 3, 2014 — MarkLogic Corporation today announced that America’s Test Kitchen has selected its Enterprise NoSQL database platform to create a searchable repository of thousands of recipes, reviews, taste tests and kitchen tips and provide access to editors of its magazines, cookbooks, websites and popular television series.
America’s Test Kitchen publishes cooking-related books and magazines, has three paid member websites, and produces two public television shows and a radio program. With MarkLogic® Enterprise NoSQL database, the publisher can now manage all of its multimedia content from a single view.
Because MarkLogic stores all content natively in XML, it eliminates the need for rigid data models allowing America’s Test Kitchen to build a powerful editorial research tool in less than six weeks. The publisher has already experienced the following benefits:
“MarkLogic provides a powerful ‘Google-like’ editorial research tool for our content,” said Guy Rochford, production director for America’s Test Kitchen. “Our editors can now search and extract articles, recipes and sidebars and repurpose content from our content management system. 80 percent of this content is based on printed magazines and books and wasn’t previously accessible.”
The world’s largest publishers are using MarkLogic to transform their businesses to digital-first organizations. By combining the flexible document storage of an Enterprise NoSQL database with built-in search engine and web services, MarkLogic speeds time-to-market, optimizes data assets, and streamlines operations – turning data into real revenue.
To learn more about MarkLogic Enterprise NoSQL database platform, attend MarkLogic World 2014, being held in your city. For more information or to register and meet with experts, attend sessions covering semantics, elasticity, tiered storage and more, please visit world.marklogic.com.
MarkLogic helps customers create value from complex data faster. Our platform ingests data from any source, creating and refining metadata to support powerful models. Customers use these models for deep search and query, building enterprise applications and bringing unique insights to analytics and machine learning.