For some years now, HP has been facing steep competition from hardware vendors, and while contemplating more efficient methods for big data analytics across their six data centers, or for enhanced customers experiences, they visualized the concept of a data lake to integrate all the data funnels from different silos into a single repository for advanced analytics. HP’s basic mission behind redesigning their enterprise data management included providing integrated big data analytics across the enterprise network, and enhancing the consumer engagement experience for increasing sales. HP’s choice of a suitable Hadoop vendor was narrowed down to MapR because they found this was the only Hadoop distribution that offered scalability, low downtime, high maintenance, and knowledgeable customer support. Keith Dornbusch, Manager of HP’s Big Data Services points out
The MapR technology was top-notch, particularly their performance and high availability features. Plus, our working relationship with the vendor was excellent. MapR’s responsiveness, service, and support, were second to none. And MapR addresses our long-term needs by being a reliable Hadoop distribution geared towards the enterprise.
Hadoop with MapR implementation at HP
The enterprise data management architecture has been built around the concept of a data lake, which is nothing but a single, centralized platform to store data derived from different customer touch points or internal departments. Paul Westerman, Senior Director of IT Big Data Solutions at HP, says, The real value is breaking down silos and bringing the data together. With Hadoop, you can drop files in and the data is there. It’s a different type of development environment. The HP Hadoop clusters are distributed over 320 servers with Dual Intel processors. The servers are capable of storing about 20 terabytes of data, while the processors provide 128 gigabytes of SD RM making the environment ideal for big data storage and processing.
