Reliable/Scalable Hadoop
The Challenge
Apache Hadoop is an ideal platform for consolidating large-scale data from a variety of real-time and legacy sources. It complements existing data management solutions with new analyses and processing tools. Enterprises today collect and generate more data than ever before and Hadoop was designed for the fast, reliable analysis of both structured data and complex data. As a result, many enterprises deploy Hadoop alongside their legacy IT systems, which allows them to combine old data and new data sets in powerful new ways.
Hadoop, however, is overly complex to deploy and its architecture, including Name Node, secondary Name Node, etc acts as a single point of failure (SPOF). Additionally, enabling multi-data center and the cloud is extremely tough with the traditional Hadoop stack, unlike Cassandra, which is designed for multi-data center and the cloud. DataStax Enterprise combines Cassandra and Hadoop to create a powerful new Hadoop distribution.
The Solution
DataStax Enterprise provides integrated Hadoop MapReduce, Hive and job and task tracking capabilities, while providing an HDFS-compatible storage layer powered by the Cassandra DB. It also exposes the full power of Cassandra for real-time applications. The result is a single integrated real time database solution that provides increased reliability, simpler deployment and lower TCO than traditional Hadoop solutions and other NoSQL databases.
The result is an extremely simple stack that eliminates the complexity and single-point-of-failure of the HDFS layer. From an operational standpoint, Cassandra is simple, every node is the same, providing a single layer in which every node is a peer of the others and automatically knows its position in the cluster.
