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data grids

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Published By: Red Hat, Inc.     Published Date: Jul 10, 2012
Today, as IT departments struggle to design and implement solutions capable of managing exponential data growth with strict requirements for application scale and performance, many of them are turning to in-memory data grids (IMDGs).
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it departments, data growth, managing data growth, application scale, application performance, in-memory data grids, imdgs, big data, data volume, data velocity, data variability, data management, data collection, data processing, data scaling, data storage systems, data access, data solutions, data challenges, management tooling
    
Red Hat, Inc.
Published By: Microsoft     Published Date: Jul 20, 2018
At its Build conference in May, Microsoft took the wraps off Cosmos DB, the new incarnation of its existing cloud-based Azure DocumentDB NoSQL database. With a nod to the dramatic, Microsoft terms Cosmos DB as its biggest database bet since SQL Server; it is positioning it as its flagship cloud database, suited for use cases ranging from security and fraud detection, to IoT (consumer and industrial), personalization, e-commerce, gaming, social networks, chats, messaging, bots, oil and gas recovery and refining, and smart utility grids. Cosmos DB is a good example of how cloud platform providers are rethinking databases for scalable, elastic environments and commodity infrastructure. The platform that is most comparable is Google Cloud Spanner, but each of these databases is engineered for different purposes: Cosmos DB as a globally distributed operational database and Spanner as a globally distributed SQL-supporting OLTP database. The highlights of Cosmos DB include its flexibility in
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Microsoft
Published By: GridGain     Published Date: Sep 20, 2018
Download the new report from GridGain to learn how in-memory computing and in-memory data grids are tackling today's data storage challenges.
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gridgain, in memory computing, imdg, data grids, data storage, customer experience, network architecture
    
GridGain
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