With time progressing, technology is transforming at a lightning speed to make the software development process much simpler, faster, qualitative, and convenient. Technologies like machine learning and AI are being utilized in several ways, not only to process qualitative and faster data but also to process predictive data. Having said that, the software development lifecycle has also evolved extensively since the last decade. Although agile is the go-to development process used by most of the development team, but since the introduction of DevOps, teams are either opting for Agile+DevOps or even implementing only the DevOps methodology.
The basic difference between Agile and DevOps is that Agile refers to an iterative approach that focuses on collaboration, customer feedback, and rapid releases while DevOps brings the development and the operations team together to manage the end-to-end engineering process. DevOps, especially in the area of testing products, brought a paradigm shift!
Introduction
Everyone loves Agile and the way it is replacing all the older methodologies and development models with a streamlined and sustainable system for faster delivery cycles. However, the ever-prevailing manual testing practice has always kept the QA teams from entirely adopting Continuous Integration and Continuous Delivery, making Agility unreachable. Fortunately, tools like Jenkins help reach the goals of the CI/CD pipeline, i.e., to maintain a continuous flow of software updates in production and shorter release cycles at reduced costs.
Jenkins and Its Relation to DevOps and CI/CD
We know that Continuous Integration and Continuous Delivery are integral parts of the DevOps process and are inevitable in the Agile approach. It has completely changed the way the development and QA team delivers software. Let us briefly review DevOps, CI/CD pipeline, and Jenkins before moving ahead.
When working on a larger project, you may struggle with the problem of an increasingly growing set of tests, which over time begins to perform slower on your continuous integration (CI) server. I had this problem while working on a project in Ruby on Rails, where RSpec tests on CircleCI took about 15 minutes.
As it was bothering me, I decided to do something about it, which resulted in building an open-source Knapsack Ruby gem library (the name derives from the knapsack problem), which deals with distributing tests between parallel CI servers. In this article, you will learn about two approaches to split tests on parallel continuous integration servers - static and dynamic.
Progressive delivery emerged as a natural response to concerns raised by the idea of “continuous” anything. If teams were going to move faster and release more often, then the surface area for things going wrong would likely be bigger. How could that be managed? And better still, how could risk be reduced while simultaneously increasing the value of moving fast? All of these questions are answered through progressive delivery.
There's a lot to be said about the culture of collaboration in DevOps.
Back in the day, system administrators had mastered the art of avoiding software developers and rejecting system changes, unless they were perfect. People said that when developers had their worst nightmares, they dreamt of a spooky unshaved admin yelling at them because of some quirky bug in their code. There were even rumors of software so refined and polished that it has never been rolled out, since operations engineers were afraid it had become too pure for us, mere mortals.
When Uncle Bob with his fellow developers formulated Agile manifesto, it was clear as day that this situation was about to change. Their aim was to stimulate better ways of developing software by doing it and helping others do it. And that they did. The statements created at the Lodge in Utah, like "individuals and interactions over processes and tools" or "responding to change over following a plan" formed the basis for a lightweight software development movement that grew up into DevOps revolution a decade later.
As a core component of continuous delivery, feature flagging empowers developers to release software faster, more reliably, and with more control. This Refcard provides an overview of the concept, ways to get started with feature flags, and how to manage features at scale.
In this post, we’ll walk through the steps for how to easily implement a CI/CD pipeline with stages that automatically build, run, and test an API Builder project.
An API Builder project is a Node.js application that can be deployed as a Docker container running on a microservice architecture.
What if you can plan for a few weeks or even a month or more at a time? You don't need the extremely short feedback cycles (hours to a day) because you're not doing high innovation. You don't need to change what the team does every few days.
You can estimate and commit to maybe a month's work at a time. On the other hand, you need to change your work more often than a two- or three-month period. That much estimation seems like a waste. And the commitment part? You laugh at that. You might like to commit, but you need to change.
To keep pace with today’s on-demand world, organizations have shifted toward modern development practices like DevOps to immediately deliver products and services to their customers. DevOps merges software development and software operations teams, so they are no longer “siloed” under one roof. With DevOps, the development and operations teams work in concert to more cost-effectively operate and evolve applications at high speed to meet marketplace customer demands.
However, many organizations are realizing that security must play an integral role in ensuring that continuous delivery practices also embrace good security processes. What good is delivering applications at such a rapid pace if sensitive customer information is left in jeopardy?
