Dell, Cloudera, and Intel Co-Engineer New In-Memory Appliance

Dell has announced a new appliance to provide customers with interactive analytics.

The new Dell In-Memory Appliance for Cloudera Enterprise is designed to provide customers with a processing engine combined with interactive analytics in a preconfigured and scalable solution, and will begin shipping Oct. 15, 2014.

The appliance allows customers to monitor, manage and gain insights in a short window of time, resulting in increased velocity for analytics. Additionally, it simplifies the procurement, deployment, and tuning of the Apache Spark solution stack, which is integrated with Cloudera Enterprise, a distribution including Apache Hadoop.

Spark is an engine for large-scale data processing that uses in-memory computing for interactive query, iterative processing, graph analysis and streaming data. According to the vendors, the integration of Spark on the Dell In-Memory Appliance with Cloudera Enterprise will allow customers to use a single tool for fast access to data, resulting in less development time on data modeling and query analysis, and simplified complex pipeline jobs.

The Dell In-Memory Appliance for Cloudera Enterprise is easy to use and is highly compatible with existing solutions, so organizations can deploy a Hadoop cluster in a couple of days for faster time-to-value in an appliance model. The solution is economically scalable from entry level up to 48 nodes without the need to rip and replace.

“Data is created and consumed at rates never before seen, and customers across all industries are struggling to ingest, store, analyze and build insights from it,” said Sam Greenblatt, vice president of Engineered Solutions and Technology, Enterprise Solutions Group at Dell. “To address these needs, Dell is moving quickly to bring appliances to market that accelerate the value customers receive from their technology by simplifying the deployment and management of large-scale enterprise applications, while at the same time ensuring best-in-class performance, response times and near real-time insights to critical business data.”

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