Event Monitoring Using AWS CloudTrail

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Introduction

If you are using cloud based services, it is evident and paramount to track events that have happened. Isn’t it?

Monitoring events in the cloud is important.

If you are using AWS, let’s assume you find that one autoscaling group in your AWS account is deleted. What will be your response?

How will you know who did it?

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SonarQube Integration with Azure DevOps

What is SonarQube ?

In simple words, SonarQube is an open-source tool for continuous inspection of code quality. It does static code analysis, provides a detailed report of bugs, code smells, vulnerabilities and code duplications.

SonarQube integration with Azure DevOps

We can utilize built-in Azure DevOps tasks for SonarQube which helps us to incorporate this tool into our CI/CD pipelines. We will learn that with a use case.

Lets begin 🙂

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Docker BuildKit : Faster Builds, Mounts and Features

It was like any other day working on micro-services project, running on Docker environment. In general, we’ve had worked on making our Image Builds more efficient, secure, and faster following basic aspects that significantly affect building and working with Docker.

  • Understanding Docker layers and structuring the Dockerfile to maximize their efficiency.
  • Reducing the weight of the Docker image, by being specific about our Base Image Tags which comes up with minimal packages.
  • Bringing the multi-stage builds concept, etc.
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Jenkins vs Azure DevOps

The world of DevOps is incomplete without ‘Continuous Integration’ and ‘Continuous Deployment’ after all these are among the building blocks of the methodology. When we talk about CI/CD the first name that comes to most peoples’ notice is Jenkins, one of the oldest and most flourished CI/CD tool in existence, however, there is one more name that’s picking up the pace as we talk, Azure DevOps, formerly known as Team Foundation Server. In this blog, we will see a detailed comparison of these two players and which one is your best fit.

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Optimize Java 8 with Docker

The majority of businesses now are using Docker to run applications. They devote a lot of time, energy, and resources to stabilize their success and invest heavily in a variety of advanced observation techniques. Despite this, they are experiencing poor performance and the containers are being stressed as a result of the heavy traffic flow. The motive of this blog is to help those running Java applications on docker containers in getting optimal performance.

A lot of times people complain that the application does not seem to be running as well as it did on the server. Here are some things to keep in mind when running a Java program on Docker.

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