Hadoop enables you to harness an unlimited volume and variety of data; Unravel provides unprecedented visibility and automation to optimize how that data is used. With Unravel, teams can understand and anticipate application behavior, resolve resource contention, and reduce performance bottlenecks — to ensure that apps that run fast and meet SLAs.
Unravel for Hadoop provides the context and 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 provides a new level of visibility 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:
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.
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:
