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To integrate Amazon SQS and Amazon Kinesis with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon SQS and Amazon Kinesis 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 Kinesis is the company’s real-time streaming data analysis platform. It provides powerful insights on video, data, audio, and other streams.
This data platform is a foundational component of data processing for machine learning, artificial intelligence, IoT telemetry, and more. With Amazon Kinesis, you have access to fully managed infrastructure to process all your streaming needs with low latency and little downtime.
It also easily connects to third-party databases and tools, like MetricFire, SQL, and Amazon Web Services (AWS) data stores.
No matter how much data you’re inputting, Amazon Kinesis easily queries and analyzes it to create flawless video, deep reporting, and even automation through Amazon S3 buckets, Redshift, Splunk, and other AWS services.
All data streams are securely encrypted and create a lot of use-cases. These proactive cognitive solutions create innovative new ways to interact with your data stores.
But why do you need it?
Amazon Kinesis gives businesses real-time access to all the necessary data for their businesses. It's an intuitive interface with visual access to all required information.
Leveraging real-time application monitoring is useful for fraud detection, tracking leaderboards, and quickly identifying issues.
Netflix uses Amazon Kinesis to keep its video streams healthy while also monitoring social media. The use cases are endless.
MetricFire easily connects to many different data sources, including Amazon Kinesis. 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 block store is doing something it shouldn't. First, you’ll need to download the Amazon AWS CloudWatch add-on from Hosted Graphite and connect your accounts.
From there, we have a comprehensive guide on how to link your accounts and set them up to automatically exchange data between both platforms.
To integrate Amazon SQS and Amazon Kinesis 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 Kinesis and get Amazon SQS and Amazon Kinesis 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
