If you work in modern software development, you probably deploy something to the cloud. These cloud deployments often lead to adopting a microservice architecture, because it enables developers to integrate cloud services more efficiently and, in turn, save time and money. That said, even if you don’t use such an architecture, your system still calls for many third-party services. And the distributed services that make up your application aren’t always easy to keep track of.

A comprehensive test suite is part of modern software development best practices. Unit tests, integration tests, end-to-end tests, and many others make sure your system keeps working when you need to change the implementation. But over the lifetime of a software project, you can end up with hundreds of such tests, and every test you add can gradually slow down your CI/CD pipeline. You have high standards for software quality, but with all these tests, your development velocity goes down the drain.

In every system, there is at least one limiting factor, as outlined in the methodology known as “the Theory of Constraints.” Software development pipelines are no exception. When we look holistically at the various domains of development, it is clear that every step is interlinked, which means the constraints of each department are also shared. Given the rise of DevOps, the importance of CI/CD, where we transition from Dev to Ops, has to be acknowledged.

With the recent trends in the observability ecosystem, monitoring your CI/CD processes became much more important. Monitoring your test performance and overall CI performance is usually the first step toward this trend. However, we think monitoring the resource metrics of the CI machine itself is important as well. For this reason, we’ve implemented a new GitHub Action to put your workflows to monitor the IO, CPU, and Memory of your CI runs.

A modern software development process consists of many steps built on each other. Two of the more significant steps in this process include integrating code from different developers into a single codebase, and deploying that codebase to some kind of infrastructure. With a CI/CD pipeline, we can automate most work related to integration and deployment, enabling high development velocity.

Hackers and cybercriminals aren’t going anywhere. In fact, they’re getting smarter and learning how to get around all of the security used by today’s businesses. The only way to protect the business is by staying ahead of the criminals and making sure any loopholes or issues are corrected fast before they can be exploited and data can be stolen or deleted from the cloud. Some of the ways to create a more secure cloud environment and stay ahead of hackers include the following.

Amazon SQS can trigger an AWS Lambda function by sending a request and can be triggered by an AWS Lambda function by receiving a message from it. We will demonstrate a scenario that realizes those two cases for an educational purpose for beginners. In this brief article, we will learn how to trigger an AWS Lambda function from an Amazon SQS queue and send a message to an Amazon SQS queue from an AWS Lambda function.