Sematext Announces Monitoring Support for Apache Spark

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sematext_logoSematext Group, Inc., a Brooklyn­ based Performance Monitoring and Log Management solution provider, has announces the first dedicated monitoring support for Apache Spark in the latest release of its SPM performance monitoring solution.

SPM for Spark monitors all Spark metrics for all Spark components – master, workers, driver, and executor. It includes alerting, anomaly detection, log correlation, custom dashboards, events graphing, custom metrics, and a lot more. SPM can be installed On Premises or one can use the Cloud version run by Sematext, in which case the setup takes less than 5 minutes before graphs with Spark (and other apps), JVM, and server performance metrics start appearing in real-­time.

Spark usage has been going through the roof,” said Otis Gospodnetic, Sematext’s Founder and CEO. “And engineers and DevOps folks handling Spark have not had a good monitoring tool at their disposal. By releasing the first Spark monitoring product to market with SPM, we have just filled a big hole in the Spark ecosystem.”

An enterprise-class solution, SPM is used by Fortune 500 and other organizations to monitor many critical Search and Big Data services, such as: Hadoop, Elasticsearch, Kafka, Solr, ZooKeeper, Cassandra, Storm, Redis and others.

One of the critical factors in the success of Spark has been the large developer community contributing to it, and the growing number of applications using Spark,” said John Tripier, Alliance and Ecosystems lead at Databricks. “We’re very excited to have Sematext join this community and contribute their expertise with a comprehensive monitoring solution like SPM.”

Also unique to SPM is that it works seamlessly with Logsene — a centralized logging, log management and analytics solution — provide a single pane of glass for performance monitoring, centralized log management, alerts, anomalies, custom events, and custom KPIs. Unlike competing products, SPM and Logsene together not only tell users when something happened, they tell them exactly what and where it happened, so engineers and operations can spend less time finding problems and more time fixing them. Most companies use a mish-mash of products from different vendors or open-source tools to achieve the same results with far more complications and for a significantly higher TCO.

Download SPM for Spark data sheet HERE.


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