Blue Data Accelerates Hadoop, Spark Deployments Fast
FREMONT, CA: Blue Data, a company that provides Big Data solutions, offers an innovative Big Data Lab Accelerator solution to simplify infrastructure and deploy the Big Data applications easily with its new software Big Data Elastic Private Instant Clusters (BDEPIC).
The Blue Data EPIC, a patent-pending software innovation includes software and professional services that changes the on-premises deployment model for Big Data making it easier, faster and more cost-effective for enterprises. It simplifies and accelerates the infrastructure deployment for Hadoop, Spark and related tools for Big Data analytics in just weeks instead of months.
With new software, the customers can evaluate Big Data tools and spin up virtual Hadoop or Spark clusters in quick time, develop, test, assure quality and provide their data scientists access to the analytical applications, data and infrastructure.
With Blue Data EPIC running in the lab on the multiple virtual machines, the user can have a two week accelerated deployment for Big Data analytics. This software reduces the time consumption with the ability to quickly and easily evaluate and test Big Data distributions, analytical tools, and use cases. The ready-to-run lab environment with up to 30 virtual nodes of your preferred Hadoop distribution(s) and/or Spark, results in up to 70 percent infrastructure savings.
"We're seeing a lot of enterprises that want to get started with Big Data analytics, and the typical starting point is a lab environment for dev/test and evaluation of initial use cases," said Kumar Sreekanti, CEO, BlueData. "With Blue Data's EPIC software, we provide a platform to simplify and streamline the deployment of Big Data analytics and infrastructure. Now we're going one step further, with a solution to jump start these lab environments and help enterprises begin their Big Data journey."
The new solution brought is ideal for any organization that starts with a dev/test lab for Big Data analytics to evaluate and experiment with various Big Data distributions, tools and applications.
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