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Optimize Hadoop With Troubleshooting and Monitoring Tools

Hadoop enables you to harness an unlimited volume and variety of data. The Unravel tool is more than a Hadoop apm, it provides unprecedented visibility, monitoring and troubleshooting automation to optimize how that data is used. With Unravel, teams can troubleshoot and anticipate application behavior, resolve resource contention, and reduce performance bottlenecks — to ensure that apps that run fast and meet SLAs.

Delivering high performance applications and data monitoring services at Hadoop scale

Unravel for Hadoop provides the context and troubleshooting automation to get maximum performance from data applications that run on the Hadoop ecosystem. Many big data teams continue to rely on Hadoop as the foundation technology for scheduling and running applications, ingesting and persisting data, and managing compute and storage resources. Unravel Hadoop tuning tools provide a new level of observability, monitoring, and understanding of the Hadoop ecosystem and the plain language context to get the most from your data.

Instant observability of the full Hadoop ecosystem

Monitoring and understanding the essential elements of a successful implementation

Hadoop is often at the center of data management for large scale data operations and applications in the modern enterprise. It interacts with an ecosystem of Hadoop services and subsystems like Hbase and Hive, as well as adjacent technologies such as Spark, Kafka, and Impala. Unravel provides unprecedented visibility into of the Hadoop ecosystem by:

  • Natively supporting every leading Hadoop distribution — Cloudera, Hortonworks, MapR, Amazon, Microsoft, and more.
  • Monitoring and tuning MapReduce and Hive applications in even the most complex processing pipelines.
  • Gaining insight into Hadoop resource consumption such as data storage, CPU, and memory usage across Hadoop clusters.

Predictive and proactive data operations for large Hadoop clusters

Predictive and proactive data operations for large Hadoop clusters

Unravel uses advanced analytics, AI and ML to develop context and predictive insights into the complete Hadoop/Spark data management and processing pipeline.

  • Predict – and prevent – runaway Hadoop jobs that reduce the performance of other applications.
  • Improve planning and budgeting with comprehensive chargeback and showback views.
  • Proactively fix common issues automatically with Auto-Actions — rogue jobs, missed SLAs, misallocated resources, cold tables, and more.

Unravel complements and extends existing Hadoop tooling

Unravel informs administrators of all Hadoop and Spark ecosystem components with useful intelligence and time saving automation

Unravel adds intelligence to all stages of Spark/Hadoop data pipelines and provides a powerful new tool set for data operations teams. In relation to existing management and monitoring tools:

  • Unravel complements existing Hadoop monitoring and management tools such as Cloudera Manager and Apache Ambari.
  • Unravel is systematic machine intelligence for the full data stack, and can use predictive analytics to catch Hadoop and Spark issues before they happen.
  • Unravel provides intelligence to take advantage of both faster and cheaper storage solutions by tracking hot, warm, and cold data sets.

Automatically tune and optimize Hadoop.

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