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To integrate Amazon Streams and Amazon SQS with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon Streams and Amazon SQS and how that can support your monitoring system.
Amazon Kinesis Data Streams (KDS) is a data streaming service that is both scalable and durable and operates in real-time. It can capture gigabytes of data-per-second from hundreds of thousands of sources, including websites, databases, social media feeds, IT logs, financial transactions, and location-tracking events. Real-time analytics tools such as dashboards, anomaly detection, and dynamic pricing are updated in milliseconds.
KDS is based around a producer/consumer model. A producer loads data records into Amazon KDS. A consumer then processes the data records from the stream. For example, a producer could load web server log data to a Kinesis data stream, where a consumer can process and analyze the log data.
You can use Kinesis Data Streams to collect log and event data from servers, desktops, and mobile devices. You can then build Kinesis Applications to process your data continuously, generate metrics, populate dashboards, and aggregate data into stores such as Amazon S3.
You can create Kinesis Applications that run real-time analytics on high-frequency event data events. For example, you could write an application to analyze sensor data collected by Kinesis Data Streams to gain insights into your sensor data.
If you collect gaming data, Kinesis Data Streams can collect data about player-game interactions and feed the data back into your gaming platform. This strategy can help you design a game that provides engaging and dynamic experiences based on players' past actions and behaviors.
With Metricfire, you can turbocharge your data stream monitoring services. By integrating Amazon CloudWatch with the Metricfire platform, you can display your KDS 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 streams are doing something they shouldn't.
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.
To integrate Amazon Streams and Amazon SQS with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon Streams and Amazon SQS and get Amazon Streams and Amazon SQS 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
