+
To integrate Amazon EMR and Pingdom with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon EMR and Pingdom 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!
Pingdom is a monitoring system that uses both synthetic and real user monitoring to deliver enhanced app and website troubleshooting.
Pingdom's synthetic monitoring allows you to simulate visitor interaction with your website or app to spot app errors or website failures before your users do. You can monitor site uptime from over a hundred locations worldwide or troubleshoot your website speed and identify webpage issues. You can also test user transactions such as shopping carts, logins, registrations, and even URL hijacking.
Pingdom's real-time user monitoring allows you to monitor how your users interact with your website or app. Pingdom's real user monitoring (RUM) is fully scalable and intuitive. Learn how your users experience your site based on device, location, and browser. Ensure you meet your SLAs or KPIs by setting your own goals and monitoring your performance. Record and compare user metrics over time to see how your website performs over weeks, months, or years.
Digital marketers can use Pingdom's synthetic monitoring to test new campaigns and ensure their users have the best experience. They can use Pingdom's real-time user monitoring to review how different user experience aspects, such as browser type or device, affect sales.
Web developers can test for website or app performance and spot defects while updates are in development. Web host providers can monitor performance in real-time to ensure client uptime. IT/Web Ops can use synthetic monitoring to quickly identify bottlenecks and spot backend issues with Pingdom's real-time user monitoring.
With MetricFire, you can amplify Pingdom's synthetic and real-time user monitoring. By integrating Pingdom with the MetricFire platform, you can display your metrics on aesthetically pleasing dashboards. With MetricFire, you can quickly understand complex websites at a glance or compare your Pingdom data with other monitoring systems.
To integrate Amazon EMR and Pingdom with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon EMR and Pingdom and get Amazon EMR and Pingdom 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
