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To integrate Amazon SQS and Amazon ElastiCache with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon SQS and Amazon ElastiCache and how that can support your monitoring system.
Amazon Simple Queue Service (SQS) is a message queuing service that you use to decouple and scale microservices, distributed systems, and serverless applications. Amazon SQS supports programmatic sending of messages via web service applications as a way to communicate over the Internet. It is fully managed and eliminates the complexity and overhead associated with operating message-oriented middleware. Amazon SQS is used by companies such as Netflix, Dropbox, NASA, and more.
With SQS, you can send, store, and receive messages between software components with the need for another service. You can get started with SQS in minutes using the AWS console, Command-Line Interface, or SDK of your choice.
SQS offers two types of message queues:
You can use Amazon SQS to add an extra layer of security to your messaging services. You can exchange sensitive data between applications using server-side encryption (SSE) to encrypt each message. Plus, because Amazon SQS scales elastically with your applications, it is a highly cost-effective messaging solution.
Amazon SQS is fully integrated with Amazon CloudWatch, allowing you to view and analyze your Amazon SQS queues metrics, which is where MetricFire comes into play.
With Metricfire, you can turbocharge your Amazon SQS 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 message queues are doing something they shouldn't.
Amazon ElastiCache is an in-memory data store and cache that supports resource-heavy applications that require sub-millisecond response times. It delivers secure and fast performance by utilizing an end-to-end optimized stack that runs on customer dedicated nodes. Plus, Amazon ElastiCache is fully managed, including tasks such as setup, configuration, hardware provisioning, software patching, failure recovery and backups. ElastiCache supports two open-source in-memory caching engines which are named Memcached and Redis.
With Amazon ElastiCache, you can quickly set up and run many popular open-source compatible in-memory data stores in the cloud. Its fully scalable architecture lets you boost your existing databases' performance or build data-intensive apps by retrieving data from low latency and high throughput in-memory data stores. For this reason, Amazon ElastiCache is often used to improve the performance of session stores, gaming apps, geospatial services, caching, queuing and Real-Time Analytics.
One of the key features of Amazon ElastiCache is its scalability, allowing you to scale-up, scale-in, and scale-out to meet your application demands. It supports both write and memory scaling with sharding.
ElastiCache continuously monitors your data storage to keep your workloads up and running, which is where MetricFire comes to the fore.
With Metricfire, you can turbocharge your Amazon ElastiCache 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 data storage cache is doing something it shouldn't.
To integrate Amazon SQS and Amazon ElastiCache with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon SQS and Amazon ElastiCache and get Amazon SQS and Amazon ElastiCache 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
