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To integrate Amazon EMR and VictorOps with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon EMR and VictorOps and how that can support your monitoring system.
Amazon Elastic MapReduce (EMR) is an Amazon Web Services (AWS) tool designed for big data processing and analysis. EMR is based on the Java-based programming framework Apache Hadoop, which supports the processing of large data sets in a distributed computing environment. MapReduce is a software framework that works by enabling developers to write programs that process vast amounts of unstructured data in parallel over a distributed cluster of processors.
EMR is used to analyze data in log analysis, data warehousing, web indexing, machine learning, financial analysis, scientific simulation, bioinformatics, among many other uses. Amazon EMR also supports workloads that are based on Apache Spark, Presto, and Apache HBase.
Amazon EMR can also be used to transform and move large amounts of data into and out of other AWS databases.
Amazon EMR has a number of benefits, including:
Monitoring Amazon EMR clusters is key to detecting critical issues with real-time applications and identifying the root causes ASAP. When you do performance monitoring, you can track how the clusters are used over time. This insight allows teams to find potential bottlenecks and optimization opportunities. Performance monitoring is your best bet to catch any issues as they happen and before they turn into major problems.
Did you know that you can integrate Amazon EMR with MetricFire? MetricFire provides a complete infrastructure and application monitoring platform from a suite of open source monitoring tools. Depending on your setup, you may choose Hosted Prometheus or Graphite and view your metrics on beautiful Grafana dashboards in real-time. Integrate with Amazon EMR to make the most of both services today!
Splunk On-Call (previously known as VictorOps), was created to make it easier for teams to manage their on-call resources. The moment something goes wrong, Splunk On-Call springs into action! Teams can receive context-rich notifications and collaborate to enable fast and efficient incident resolution with reduced downtime. Stakeholders can view critical incidents and the steps taken to resolve their issues.
The on-call experiences of both DevOps teams and clients become much more satisfying with Splunk On-Call. Its fast incident response features quickly deliver the right alerts to the right people reducing the time it takes to resolve incidents. You can integrate Splunk On-Call with your existing tools such as MetricFire to manage incident timelines and reporting for blameless post-incident reviews. As well as improving client incident resolution experience, Splunk On-Call's notification and incident management features reduce employee burnout. Mobile-first notifications and machine learning tools help make being on-call easy and accessible no matter where your team members are located.
Splunk On-Call comes with a range of incident analysis and reporting tools. With reports like MTTA/MTTR, Incident Frequency, and Post-Incident Review, teams can manage incident hotspots and improve incident resolution. You can monitor team and individual performance metrics, review incident frequency, and create reports for post-incident reviews.
With Metricfire, you can turbocharge your Splunk On-Call monitoring tools. By integrating Splunk On-Call with the Metricfire platform, you can display your metrics on aesthetically pleasing dashboards. MetricFire's advanced filtering lets you choose only the data views you want to see and discard the rest. You can also set up simple rules to discard data you no longer keep, plus receive alerts via email or Slack.
To integrate Amazon EMR and VictorOps with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon EMR and VictorOps and get Amazon EMR and VictorOps interacting with your MetricFire dashboards directly.
MetricFire is a full-scale platform that provides infrastructure, system, and application monitoring using a suite of open-source monitoring tools. We enable you to use Hosted Graphite and aesthetic custom dashboards to visualize your metrics so you can understand what is happening.
MetricFire offers users a complete ecosystem of end-to-end infrastructure monitoring, comprised of popular open-source monitoring software services: Graphite and popular dashboards. Plugins for many other open-source projects are preconfigured, such as StatsD, collectd, and Kubernetes. You get all these within a hosted environment as a single product. Not only does MetricFire fit well into the infrastructure monitoring use-case, such as network monitoring and server monitoring, but we also do application monitoring and business intelligence.
Through this hosted environment, MetricFire boosts the unique features of open-source projects to give you more functionality than the original products. Below are some of the MetricFire features at a glance:
The key thing to remember is that Hosted Graphite by MetricFire is more than just Graphite. Our Hosted Graphite product actually adds data dimensionality and better data storage.
The benefits of MetricFire are:
“We now have over ten times the amount of metrics we started with, and on different accounts. One of the great things about MetricFire is that scaling to support this increase has been hassle-free, requiring no additional work on our side.”
“Building and managing an on-premise installation at this scale would require a lot of engineer time, especially in the first year...we use this engineering time to work on initiatives closer to our core business”
“There’s complete transparency with everything MetricFire do which means we can accurately predict what we’ll be spending and comfortably keep within our budget.”
"Every time I have a question, I get an answer from support after just a couple of hours. Their technical knowledge is excellent.”
In this article, we’ll discuss what can go wrong with our machine-learning model after... Continue Reading
