Unravel Data introduced a full-stack, artificial intelligence (AI)-driven solution for big data workloads running on Amazon EMR.
Amazon EMR is a cloud service that enables big data processing using Spark, Kafka, Hadoop, Impala, Hive, and other big data components across dynamically scalable Amazon EC2 instances.
Unravel APM for Amazon EMR is immediately available on the Amazon Web Service (AWS) Marketplace and includes a 30-day free trial license. Seamless integration with the Amazon EMR environment enables users to connect Unravel to a new or existing EMR cluster with just one click. Unravel APM for Amazon EMR can improve the productivity of big data teams with a simple, intelligent, self-service Performance Management capability.
Unravel APM is engineered to:
- Automatically fix slow, inefficient and failing Spark, Hive, MapReduce, HBase and Kafka Impala applications
- Reduce Amazon cloud expenses by automatically eliminating wasteful resource consumption by users and applications
- Get a detailed chargeback view to understand cluster resource usage by user, department or project
For enterprises powered by modern big data applications, the Unravel platform accelerates the adoption of big data in the cloud by operationalizing the Amazon big data infrastructure.
Using AI, machine learning and other advanced analytics, Unravel APM ensures enforceable service level agreements (SLAs) and drastically lower compute, I/O, and storage costs. Furthermore, it reduces operational overhead through advanced automation, and predictive maintenance, enabled by unified observability and AIOps capabilities.
Kunal Agarwal, CEO of Unravel Data, said: “As enterprises plan and execute their migrations to the cloud, Unravel enables operations and development teams to improve the performance and reduce the risks commonly associated with migrating modern data applications to the cloud. As we help resolve difficult and cumbersome IT processes, we can enable organizations to realize and attain optimal deployment models and to evolve those models with confidence over time.”
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