Real Time & Analytics
The Challenge
The need for unification between low latency and analytical infrastructure is clear. Data movement (ETL) is a productivity killer. Until now there was no good answer; only compromises. Either companies were forced to suffer the complexity of moving large volumes of data between their systems (in the typical ETL pipeline) or they had to make do with the limited real-time functionality within the Hadoop framework (primarily HBase) that provides some table abstractions but is complex to operate and optimized for scanning workloads more appropriate to analytical jobs rather than the random access performance critical for real-time applications. Given this choice, most opted for two systems and ETL.
The Solution
DataStax Enterprise is unique in that it can serve as the primary database receiving direct input from online applications, while offering analytic, Hadoop MapReduce functionality for analysis on that same data. DataStax Enterprise provides integrated Hadoop MapReduce, Hive and job and task tracking capabilities, while providing an HDFS-compatible storage layer powered by Cassandra. It also exposes the full power of Cassandra for real-time applications. The result is a single integrated solution that provides increased reliability, simpler deployment and lower TCO than a traditional Hadoop solution.
This extremely simple stack eliminates the complexity and single-point-of-failure of the HDFS layer. From an operational standpoint, there’s no need to setup a name node, secondary name node or the rest. Cassandra provides a single layer in which every node is a peer of the others and automatically knows its position in the cluster.
