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To integrate Amazon Firehose and circleci with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon Firehose and circleci and how that can support your monitoring system.
With the ever-increasing popularity of streaming content, the need to enable users to collect, store, capture, and process large amounts of data from their distributed streams is crucial. By using Amazon Kinesis Data Firehose, you can capture massive amounts of live video data from millions of sources, and then make them available to consumers in a data-efficient and cost-effective format.
Amazon Kinesis Data Firehose lets you quickly load streaming data into data stores, data lakes, and analytics services. You can use it to capture, transform, and deliver streaming data to a range of Amazon services, including Amazon Redshift, Amazon S3, Amazon Elasticsearch Service. You can also use Kinesis to connect to generic HTTP endpoints and service providers such as New Relic, MongoDB, Datadog, and Splunk.
Firehose is a fully managed service that automatically scales to match your data throughput. You can also use it to compress, transform, and encrypt your data streams. This feature minimizes the amount of storage required for your streaming data and increases security.
Amazon Kinesis Data Firehose integrates with Amazon CloudWatch metrics so you can collect and analyze your data streaming metrics. For example, you could monitor the IncomingBytes and IncomingRecords metrics to track data ingested into Kinesis Data Firehose from your data producers. You can use Kinesis Analytics to analyze and process your data streams using standard SQL. It also comes with pre-built stream processing templates to help you manage your data analytics.
By integrating Amazon CloudWatch with the MetricFire platform, you can display your streaming 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 need, plus receive alerts via email or Slack when your data streaming is doing something it shouldn't.
CircleCI is a continuous integration and delivery platform (CI/CD) that you can install in a private cloud or data centers.
After you authorize a software repository, such as GitHub or Bitbucket, as a project on circleci.com, every code change you make triggers automated tests in a clean container or VM. After the tests are complete, CircleCI notifies you via email of successes and failures.
You can configure CircleCI to deploy code to various environments, including AWS CodeDeploy, AWS EC2 Container Service (ECS), AWS S3, Google Kubernetes Engine (GKE), Microsoft Azure, and Heroku. You can deploy other cloud services using SSH or by installing the service's API client with your job configuration.
System administrators can gather metrics for monitoring their CircleCI installation for various environment variables, including installed Nomad clients and Docker metrics. Metrics collected by CircleCI include basic information, such as CPU or memory usage, and more advanced metrics, such as the number of executed builds or the number of internal errors. By recording and analyzing metrics, you can quickly detect incidents and abnormal behavior, retroactively understand infrastructure-wide issues, and scale computing resources dynamically.
With MetricFire, you can turbocharge your CircleCI monitoring to a new level. One useful method of monitoring the changes in your codebase is by adding annotations whenever your tests run. For example, by adding CircleCI annotations to your MetricFire graphs using a webhook, you can keep track of which passed tests have affected your system performance (negatively or positively). This method can help you prioritize what to fix first, as a highly negative impact should have top priority.
To integrate Amazon Firehose and circleci with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon Firehose and circleci and get Amazon Firehose and circleci 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
