Solution brief

Improve the economics of Big Data

Get better price/performance for Big Data Analytics running on the Hortonworks Data Platform

Expect more from your data

Today's data-driven enterprises need to store, access, and analyze massive amounts of structured and unstructured data from multiple sources. This has created the need for a new generation of data center architecture; one where all your data is stored in a vast, ever-expanding data lake, creating a common data set for analytics and applications. Apache Hadoop was born to address this need, providing an open source, standards-based data platform with the ability to store and analyze all types of data at a low cost. But many organizations encounter challenges and limitations when deploying Hadoop.
As Hadoop adoption increases, it often proliferates into multiple clusters running different workloads on a variety of technologies and Hadoop distributions, leading to challenges with data duplication and cluster sprawl. So as Hadoop use cases and capabilities expand, enterprises need to improve performance to run diverse sets of workloads and consolidate data and infrastructure with the ability to scale across a common, elastic, shared, and flexible infrastructure.

Versatility and agility for modern data centers

The HPE EPA with HPE ProLiant DL325 Gen10 servers, powered by AMD EPYC processors and the Hortonworks Data Platform (HDP) distribution of Hadoop, provides a scalable multi-tenant platform.