Distributed Processing and Analytics
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Apache™ Hadoop® enables big data applications for both operations and analytics and is one of the fastest-growing technologies providing competitive advantage for businesses across industries. Hadoop is a key component of the next-generation data architecture, providing a massively scalable distributed storage and processing platform. Hadoop enables organizations to build new data-driven applications while freeing up resources from existing systems.
There are two main layers to Hadoop, the compute layer that consists of an expanding group of packages of open source components (Hive, Pig, YARN, Oozie, Sqoop, Flume, etc…). The second layer is the Hadoop Distributed File System (HDFS). Most of the focus tends to be on the compute and its corresponding robust, thriving ecosystem. However, the data layer is an important and often overlooked aspect of Hadoop. In fact, HDFS has major limitations today that existed when Hadoop was created 10 years ago. Hadoop lacks enterprise grade features, including consistent snapshots, and mirroring for DR. It’s a batch file system not real-time, and legacy applications cannot use it like a standard file system.
MapR has addressed these limitations with the Converged Data Platform that provides an underlying data layer for Hadoop that is real-time, enterprise-grade, fast, and scalable.
Change the Economics of Your Data
More Data Leads to Better Insights
Game-Changing Business Applications
Hadoop is Transforming Businesses Across Industries
Every industry is benefitting from the scale and processing power that Hadoop as part of a converged data platform brings, becoming more data-driven and gaining deeper insights to customers and operations.