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To integrate Amazon Lambda and Amazon RDS with your monitoring system, please reach out to MetricFire. Book a demo with the MetricFire team to discuss integrating Amazon Lambda and Amazon RDS and how that can support your monitoring system.
Amazon Lambda is a cloud-based, fully scalable service that lets you run code without managing or provisioning servers. Amazon Lambda will run your code when you need it and only charges you when you use it. You can run virtually any code for nearly all types of backend applications and services with no administration. Amazon looks after it all for you, including code monitoring and logging, capacity provision and scaling, and operating system and server maintenance.
You can use code that responds automatically to an Amazon DynamoDB table or Amazon's Simple Storage Service (Amazon S3). You can also invoke code using API calls made using AWS SDKs or write code to respond to HTTP requests using Amazon API Gateway. Plus, you can write functions for serverless applications that you can automatically deploy using AWS CodeBuild and AWS CodePipeline. You can create backend services that take advantage of Amazon Web Services inbuilt security, scale, and performance, plus use Amazon Lambda to extend other AWS services with custom logic.
More importantly, Amazon Lambda automatically monitors your code on your behalf and reports metrics through Amazon CloudWatch. Amazon Lambda automatically tracks the number of requests, the invocations per request, and the number of errors triggered by a request.
With MetricFire, you can turbocharge your Amazon Lambda code monitoring services. By integrating Amazon CloudWatch with the MetricFire platform, you can display your Amazon Lambda 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 you can receive alerts via email or Slack when your code is doing something it shouldn't.
The Amazon Relational Database Service (Amazon RDS) is a web service you can use to set up and manage relational databases in the AWS Cloud. It provides a cost-efficient way to leverage fully managed and scalable industry-standard relational databases without the need to look after everyday database administration tasks.
When you subscribe to Amazon RDS, you get your own cloud-based server, CPU, memory, storage and IOPS, all bundled together. You can set up, manage, and scale each of these services independently. If you need more CPU or more storage, you can quickly allocate them. Amazon RDS manages your backups, software patching, plus it includes automatic failure detection and recovery. You can use different database flavors, including MySQL, MariaDB, PostgreSQL, Oracle and Microsoft SQL Server.
You can configure Amazon RDS for high availability and failover by setting up a primary instance and a synchronous secondary instance. Plus, you can control who can access your RDS databases by using AWS Identity and Access Management (IAM) services to assign users permissions.
Amazon RDS is fully integrated with Amazon CloudWatch, allowing you to monitor your database's health and performance. By subscribing to Amazon RDS events, you can be notified about changes to a DB instance, DB parameter group, DB security group or DB snapshot.
More importantly, with Metricfire, you can turbocharge your Amazon RDS 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 a database issue is detected.
To integrate Amazon Lambda and Amazon RDS with your monitoring system, sign up for a free trial with MetricFire. Talk with the MetricFire team about how to integrate Amazon Lambda and Amazon RDS and get Amazon Lambda and Amazon RDS 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
