In today’s world of software development, one of the most emphasized practices is CI, or Continuous Integration. Continuous Integration is the first step of the CI/CD pipeline and acts as an enabler for the whole DevOps mindset and methodology. CI is the foundation of modern software development. Given the fact that this is the first stage of a proper DevOps setup, it’s crucial that it must be done correctly.

This tutorial is a follow-up to TurboCharging your Android Gradle builds using build cache . The key focus of this post is the remote build cache, a build speed acceleration technology that can be implemented for both local and CI builds. This is a technology worth knowing about because: Gradle provides a build cache node available as a Docker image. You can host this image in a number of ways.

Security vulnerabilities and license violations should be found as early as possible and the earlier in the SDLC , the better. As part of the “ Shift Left ” vision, JFrog CLI and Xray now allow scanning dependencies directly from sources , on-demand, using a simple command line. This functionality allows benefiting from the same JFrog Xray vulnerability and license scanning capabilities, even before deployment to JFrog Artifactory.

The cloud native promise to be able to “build once, deploy anywhere” is nearly fulfilled. With containerization and Docker , we can build our applications and services for any environment, and set configuration at runtime. Well,… almost. Operating systems and apps still need to be compiled to execute on specific architecture types. Your software that’s been compiled for an AMD64 processor can’t run on an ARM-based machine, nor can one built for Linux run on Windows.

The DevOps, IT security and IT governance communities will remember 2021 as the year when the Software Bill of Materials , or SBOM, graduated from a “nice to have” to a “must have.” Around for years, the SBOM has now become a critical DevSecOps piece, which everyone must thoroughly understand and incorporate into their SDLC (Software Development Lifecycle).

Virtualization has been with us since the 1960s. In computer science terms, it is about as old as the hills. Yet this old-school idea has never been more relevant. We all know hardware is expensive. Despite the cost, many businesses squander their precious processing power. All too often, a “one server, one purpose” mindset leads IT departments to waste server capacity. With virtualization, we leave this myopic view of server management in the dust.

Modern software development teams use CI/CD tools to ship features quickly and rely on best practices like shift-left testing to find application errors before they become user-facing bugs. But you still face the risk that any code you deploy could contain errors that your testing did not surface. To help you deploy with confidence and mitigate the effects of a bad deployment, Datadog APM now provides Automatic Faulty Deployment Detection.

I have been in the IT industry for a few decades now and have helped launch waves of technology in the constant pursuit of making computing easier, cheaper and with greater uptime. This all started well before my entry into the IT industry and will continue to well past the time I retire. However, it is always good to understand where we have been and look how far we have come to understand how we can continue to make it even better.

Most interactions with a web application occur at the browser. Users search items, fill forms, create shopping carts, log into their profiles, and perform many other tasks. Unit tests are great, but nothing beats testing an application by interacting with the user-facing frontend. In this tutorial, you will learn and demonstrate how to write tests for the browser using Puppeteer . You will then take this a step further by automating the testing process in a continuous integration flow.

Thousands of secrets leak daily on public git repositories, including over two million corporate secrets in 2020 alone. This can happen to anyone! For example, in January 2021, an Amazon cloud engineer accidentally committed almost a gigabyte worth of sensitive data that included their own personal documents, as well as passwords and cryptographic keys to various AWS environments on his personal GitHub repository.