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To integrate Amazon EFS and PagerDuty with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon EFS and PagerDuty and how that can support your monitoring system.
Amazon Elastic File System (EFS) provides a scalable but easy-to-use elastic NFS (network file system) for AWS Cloud services and on-premises resources. It supports on-demand requirements to the petabyte scale. It is fully managed and can shrink or expand as you add and remove files. This high level of change removes the need to manage capacity to accommodate growth.
Amazon EFS provides parallel shared access to thousands of Amazon EC2 instances, allowing your applications to achieve high aggregate throughput levels and IOPS (Input/output operations per second) with consistent low latencies.
You can use Amazon EFS for almost any purpose, from small home storage to business-critical applications, including big data analytics, database backups, application development, testing, web serving, content management, media and entertainment workflows, and container storage.
You can monitor your Amazon EFS file systems using Amazon CloudWatch. Metrics include the number of client connections, the amount of data read or written, file system storage, and more. You can use your Amazon EFS console to display a series of graphs based on the raw data from Amazon CloudWatch. For more extensive monitoring, you would find MetricFire an excellent companion thanks to the more advanced features.
With Metricfire, you can turbocharge your Amazon Elastic File System monitoring services. By integrating Amazon CloudWatch 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, scraping the unwanted data. You can also establish clear rules to toss out data you no longer have, plus get custom alerts via email or Slack when your EFS files system falls out of alignment.
PagerDuty is an incident management platform used to help teams quickly detect and fix infrastructure problems. It manages automatic notifications when a problem occurs, escalates the issue where necessary, and schedules on-call resources.
With its SaaS platform, PagerDuty provides businesses, developers, and administrators a range of powerful tools to help them quickly manage client-impacting issues. By arming your business with the insight to proactively manage events that may affect customers across your IT infrastructure, PagerDuty helps you to maintain customer experience and satisfaction. It also allows you to sustain your brand reputation and revenue streams.
PagerDuty comes with hundreds of native integration points to ensure that the right data gets in the hands of the right people in real-time. By taking advantage of machine-learning, PagerDuty automatically handles resource scheduling and issue escalations. You can customize PagerDuty notifications to use a combination of phone calls, SMS, push notifications, and emails depending on the issue severity, time of day, or defined support hours.
These features are why PagerDuty can be such a useful companion to your MetricFire applications such as Hosted Graphite. For example, you might use Hosted Graphite to receive alerts about system performance, code bugs, or other issues. If you use PagerDuty as well, you would find it of immense use when you wanted automatic delegating to the people in your organization. One way would be to use PagerDuty to create alerts that it automatically assigns to specific people on your team.
You can create a PagerDuty service on MetricFire's Hosted Graphite that triggers once an alert criterion reaches a specified threshold. Once triggered, the service delegates tasks to specific individuals, leaving you free to focus on other important job functions.
To integrate Amazon EFS and PagerDuty with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon EFS and PagerDuty and get Amazon EFS and PagerDuty 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
