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Agile Computing Authors: Liz McMillan, Elizabeth White, ManageEngine IT Matters, Carmen Gonzalez, Yeshim Deniz

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@CloudExpo: Article

Compuware APM Extends Leadership in Big Data

Delivers immediate insight into Big Data deployments to speed problem resolution, optimize performance and reduce risk

Compuware Corporation on Wednesday announced that Compuware APM for Big Data now offers enhanced support and out-of-the-box dashboards that enable organizations to optimize big data projects through unmatched visibility into Hadoop, NoSQL and Cassandra deployments. Now organizations have deeper insight into big data workloads and transactions to quickly find the root cause of slow jobs and failures in minutes, instead of hours or days.

Enhancements for Hadoop enable operations teams to gain deep insight into the most active users in a cluster with automatic profiling of intensive jobs. Problem patterns, including data shuffle across the network, can be quickly identified as well as resource utilization and tracking to enable charge-back models.

New enhancements in Compuware APM for Big Data include:

  • Enhanced out-of-the-box, zero configuration dashboards for Hadoop, with direct correlation of map and reduce tasks to users, pools, queues and jobs. This gives users unprecedented insight into performance of the cluster in relation to Hadoop-specific metrics and resources consumed by jobs.
  • Support for Hadoop 2 and Hortonworks Data Platform (HDP) 2.0, the only Hadoop distribution that provides support for Windows.
  • Greater insight into Hadoop Distributed File System (HDFS) and how data is moved across the cluster. This allows organizations to identify problem patterns with data locality and ensure distribution is optimized.
  • Full support for Cassandra, including recently released CQL3. Companies can also optimize end-to-end transactions using the latest versions of MongoDB, Hbase and many other NoSQL databases.

Hortonworks is partnered with Compuware APM to provide customers with faster, less complicated development cycles by virtue of complete visibility into the performance of their MapReduce jobs.

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