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To integrate Amazon CloudWatch and Amazon ELB with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon CloudWatch and Amazon ELB and how that can support your monitoring system.
Amazon CloudWatch is a management and monitoring service designed for AWS and other infrastructure resources or on-premises applications. It is the official metrics monitoring tool for Amazon Web Services. Using CloudWatch, you can access all your performance and operational metrics in a single platform, helping you overcome the challenge of monitoring multiple systems. CloudWatch helps you monitor your entire stack — including applications, infrastructure, and services — thus freeing up valuable resources to allow you to focus on building applications.
You can use CloudWatch Container Insights to monitor and troubleshoot your applications and microservices. CloudWatch collects, aggregates, and summarizes computer utilization information; like CPU and memory usage, network data history, and also monitoring diagnostic information. Container Insights provides you with details about container management services, such as: Amazon ECS for Kubernetes (EKS), Amazon's Elastic Container Service (ECS), etc.
The brilliant thing about Amazon Cloudwatch is that it is your gatekeeper to data and metrics for all your Amazon applications and services. However, monitoring more than the standard set of metrics can become very expensive with CloudWatch. CloudWatch custom metrics are very expensive and they should be used sparingly. For example, if a company is monitoring their AWS systems with the standard CloudWatch dashboards, it might cost around 1000 USD a month. However, if you’re monitoring hundreds of thousands of metrics related to a new launch, AWS CloudWatch could quickly rack up to 50,000 USD a month.
That's why it's such a vital integration point for MetricFire. MetricFire treats all metrics the same, so if you’re monitoring thousands of specialized metrics, you’ll still pay the same basic rate for those metrics. CloudWatch can be integrated with MetricFire, so you can pull your AWS metrics into the MetricFire platform. Then, you can get low-cost metrics scaling, while still being able to monitor your AWS metrics all in a single pane of glass. 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 need to keep, plus receive alerts via email or Slack.
Amazon Elastic Load Balancing is a tool that automatically distributes incoming application traffic across multiple targets, including Amazon EC2 instances, containers, virtual appliances, Lambda functions, and IP addresses. Your application traffic can be handled by a single availability zone or across multiple zones.
There are four types of load balancers available with Amazon Elastic Load Balancing:
Amazon Elastic Load Balancing monitors your applications' health and performance in real-time with Amazon CloudWatch metrics, logging, and request tracing. These monitoring tools help you analyze your applications' behavior, uncover issues, and identify performance bottlenecks in your application stack. By using Elastic Load Balancing, you can also ensure compliance with application Service Level Agreements (SLAs).
With Metricfire, you can turbocharge your Amazon Elastic Load Balancing 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 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 when your load balancing is doing something it shouldn't.
To integrate Amazon CloudWatch and Amazon ELB with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon CloudWatch and Amazon ELB and get Amazon CloudWatch and Amazon ELB 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
