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November 26, 2018 Adds Grafana For Incident Detection

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Log analysis specialist is launching a metrics application based on the Grafana open source tool used to crunch time-series data. The new metrics capability it being added to its flagship log analytics platform that combines machine learning with the open-source ELK (Elasticsearch, Logstash, Kibana) log analyzer.

The Israeli company said the combination of Grafana and ELK would help users consolidate IT monitoring, troubleshooting and security on a single platform. The security tool responds to the complexity associated with deployment of distributed applications via containers and other microservices as well as the uptick in hybrid and multi-cloud deployments.

The metrics tool released on Monday (Nov. 26) seeks to reduce the growing amount of IT resources committed to deploying and monitoring infrastructure at the expense of technical innovation. The ELK-Grafana combination is promoted as a way automating monitoring while boosting the performance and security of distributed applications.

“Performance issues, service outages and security breaches can cost millions and severely damage customer trust,” said CEO Tomer Levy. “Yet the engineers entrusted to manage these functions lack an easy, scalable option and are frustrated by the limitations and complexities of current open source monitoring and troubleshooting systems.”

The Tel Aviv-based company is offering ELK and Grafana as cloud services that scale more easily than in-house monitoring frameworks. The combination also designed to work with individual technology stacks via integrations with applications container, the Kubernetes cluster orchestrator and other cloud-native tools along with incident management platforms and other security tools.

The platform unveiled a year ago synthesizes machine data with user behavior and “community knowledge” to track potential incidents while limiting the amount of data stored and analyzed.

The requirement to quickly detect and repair IT incidents is growing with the introduction of microservices and other agile approaches to continuous delivery of enterprise applications. According to a Splunk-sponsored (NASDAQ: SPLK) study released last year, the average organization logs about 1,200 IT incidents per month, of which only a small percentage are deemed critical.’s approach is touted as making greater use of machine learning tools to speed incident detection. The company said last year its app insights tool uses machine learning to create a model for “normal operations,” then isolates errors that don’t fit the model.

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