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To integrate GitHub and Amazon Firehose with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating GitHub and Amazon Firehose and how that can support your monitoring system.
GitHub is a service for hosting code and doing version control for code. The foundations of GitHub is "Git" - which is an open source version control system.
Git allows multiple coders to contribute to one project, while remembering which is the most up-to-date version. Git also remembers who contributed and what they contributed to the project. All modifications and revisions are stored in a central repository, and contributions are made through a pull request.
Pull requests can be made by anybody, but they must be approved by the repository admin. In some projects, pull requests can be contributed without approval. However, most projects have some quality assurance processes.
When doing application performance monitoring for a project built in GitHub, you’ll want to export data about your GitHub setup into your regular monitoring dashboards. You want to monitor when changes happen, and the effect they have on your system.
For example, if you make a change to your code, and suddenly the latency of requests to your server increases - you’ll want to know exactly which pull request caused that change. If you didn’t know that there were any changes in your code, then you wouldn’t be able to correlate the change in your code with the changes in how your system is behaving. This will slow down your debugging process.
With MetricFire, you can get all of your monitoring dashboards automatically annotated with the changes you’ve made to your code in GitHub. If there are any changes in behavior, a small note will be present in the graph to tell you what’s happening in your environment.
This enables you to automatically monitor the correlation between GitHub changes and how your system is functioning.
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.
To integrate GitHub and Amazon Firehose with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate GitHub and Amazon Firehose and get GitHub and Amazon Firehose 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
