Big Data Infrastructure

Innovative approach to agile
information processing

Capacity Management

Optimize hardware utilization and prevent cluster sprawl by creating standardized blueprints. Define resource and utilization policies per project based on the application requirements.

Self-Serve Access

Data engineering teams can independently create clusters, load data sets, and run their batch-processing or streaming iobs.

Application Gallery

Enable teams to provision and scale new, big data applications quickly with a modern, self-serve application gallery— Apphub™.

Empower your Data Scientists
to unlock new business potential.

The 21st century enterprise relies on big data to develop effective strategies, ensure future growth, and respond to competitive pressures. However, traditional IT systems cannot provide the agility Data Scientists need to process constant streams of data and extract actionable insights. IT departments spend weeks to months installing and configuring Hadoop applications on a rigid, complex, and expensive infrastruture—by the time the data is analyzed it’s already old news.

We believe your data should give you a competitive edge with quick, actionable insights into the future of success. eFabric lets your data scientists explore and model vast amounts of data with an innovative, agile, and cost-effective way of processing information.

It starts with blueprints—a standardized template set by IT administrators that defines resource utilization policies per project requirement. This optimizes hardware utilization, reduces costs, and prevents cluster sprawl. IT administrators then create secure, network isolated zones within the given infrastructure, so that teams have shared and secure access to all relevant information. Once this is done, Data Scientists have access to their own dashboard through AppHub—an application gallery with one-click access to Hadoop, Spark, and other big data apps. With a click of a button, Data Scientists can instantly create clusters, load data-sets, run batch-processing of data, and more—without IT being present.

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