AWS observability: AWS resiliency monitoring best practices

AWS observability: AWS monitoring best practices for resiliency

What is AWS observability? And why it matters

The ability to investigate the current state of your AWS environment using data created by it, such as logs, metrics, and traces, is referred to as AWS observability.

Due to their matrix of cloud services across many environments, AWS and other multi-cloud configurations may be more challenging to maintain and monitor than traditional on-premises infrastructure. To deal with this problem, IT employees need a thorough grasp of what’s happening, the context in which it’s happening, and the affected individuals. Teams may use accurate, contextual observability data to create data-driven service-level agreements (SLAs) and service-level objectives (SLOs) to make their AWS infrastructure reliable and robust.

AWS: A service for everything

AWS offers a set of technologies and serverless tools for executing modern apps in the cloud. Here are some of the most well-known.

• Amazon Elastic Compute Cloud (EC2). Amazon’s EC2 compute platform is an Infrastructure-as-a-Service (IaaS) platform that can handle any workload at scale. With EC2, Amazon takes care of the basic computation, storage, networking, and virtualization infrastructure, while you care about the OS, middleware, runtime environment, data, and applications. EC2 is great for heavy workloads with a steady stream of traffic. 
• Amazon Web Services offers AWS Lambda as a service. Lambda is an event-driven, functions-as-a-service (FaaS) compute service from Amazon that runs code for application and back-end services when they are triggered. AWS Lambda makes it simple to create, execute, and maintain application systems without installing or managing infrastructure. 
• Amazon Fargate. Fargate is an AWS container serverless compute environment. It controls the underlying infrastructure supporting distributed container-based applications, allowing developers to concentrate on innovation and application development. Also, the frigate is designed to operate containers with smaller workloads and on-demand consumption on a limited basis. 
• EKS is Amazon’s managed containers-as-a-service (CaaS) for Kubernetes-based applications running on-premises or in the AWS cloud. Later, controllers on the managed Amazon EKS control plane, which manages container orchestration and scheduling, are used to integrate EKS with AWS Fargate. 
• Amazon CloudWatch. CloudWatch, and Amazon Web Services monitoring and observability service keeps track of applications, resource usage, and overall system performance for AWS-hosted settings. You’ll need a different method if you’re using non-AWS technology or need more breadth, depth, and analysis for your multi-cloud setup than AWS and CloudWatch can provide. 

Serverless solutions can help to simplify management. However, just like any other production tool, it’s vital to understand how these technologies interact with the rest of the stack. When a user receives an error page on a website, it’s critical to track down the source of the problem.

While AWS provides a basis for executing serverless workloads and collaborative tools for monitoring AWS-related workloads, it lacks complete instrumentation for multi-cloud observability. As a result, if there isn’t enough monitoring, other application performance and security issues can go unnoticed.

AWS monitoring best practices

Software engineers frequently use application instrumentation frameworks to obtain insight into these issues. These frameworks provide insight into applications and code. Breakpoints/debuggers and logging instrumentation are examples of frameworks, as are activities like manually reading log files. The manual approach is usually only effective in smaller workplaces with restricted applications. Here are some best practices for ensuring AWS observability in more significant, multi-cloud setups.

1. Utilize CloudWatch data to its best potential. As part of the monitoring plan, use CloudWatch can collect monitoring data from across all components of your AWS system so you can debug any errors. The ability to investigate the current state of your AWS environment using data created by it, such as logs, metrics, and traces, refers to as AWS observability. While this allows for more scalability than on-site instrumentation, it also adds complexity. Multiple tools necessitate teams collecting, curating, and coordinating data sources from several locations. 
2. Can automate monitor tasks. There are so many AWS services and external technological links. Crews also are increasingly turning to observability and monitoring solutions that can automate more. Units must be able to separate the “unknown unknowns automatically.” Bugs that haven’t discover yet can’t detect by dashboards. Also, they don’t lend themselves to quick and easy remedies that are unknown. 
3. Make a separate plan for monitoring EKS and Fargate. Users are in charge of identifying and replacing faulty nodes, applying security updates, and upgrading Kubernetes versions with EKS. Before, if you have a lot of EKS clusters, you should automate some of the manual tasks. It immediately discovers bottlenecks or faults. Consider an all-in-one solution that integrates with AWS and your other Kubernetes settings. It must achieve the highest level of EKS observability on Fargate. By merging analytics technologies like application metrics, distributed tracing, and real-time user monitoring with Kubernetes clusters, nodes, and pods, the goal is to provide automatic observability in Kubernetes clusters, nodes, and pods.

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