Skip to main content

What is Jenkins, and how to use it for DevOps

Jenkins is an Open Source software written in Java.

It provides services like Build Management, and can be used for running tests (functional or UI). It is also used for CI (Continuous Integration) as well as CD (Continuous Delivery).

Jenkins is a free tool, easy to install and configure. We can add various plugins which can help in integrating different tools like Azure DevOps, GitHub (for source control), Maven, Ant, MS Build (for build as well as testing), Selenium (UI automation), Ansible (for deployment) and many more. Using Jenkins, it becomes easy to integrate all kinds of tools for build, testing, packaging, analyzing, deploying etc.

In this two-part tutorial series, I will discuss integration of Jenkins with various tools such as:


  •     Azure DevOps and GitHub: for Source Control
  •     Java with Eclipse and C# with Visual Studio 2017: for code writing
  •     Apache Ant, Apache Maven and MS Build: for Build Management
  •    Junit 4.12 with Eclipse and MS Test with Visual Studio 2017: Functional testing
  •     Selenium with Eclipse and Selenium with Visual Studio 2017 C#: UI Testing
  •     GitHub (webhooks) and Azure DevOps (service hooks): Continuous Integration

Before we delve into Jenkins, I want to provide some information about Azure DevOps.


Overview of Azure DevOps


Azure DevOps (formerly called Visual Studio Team Services – VSTS) is a set of services for developing, testing and delivering products. Using Azure DevOps, creating and deploying applications become quite efficient. It is not only a set of tools for automation of CI CD using Microsoft stack, but a lot of other third-party tools can very easily integrate with it.

In fact, if our build pipeline is on Azure DevOps, we can integrate it with Jenkins to perform some job. A few years back, it started as Team Foundation Server (TFS) on the cloud, but over the years, it has evolved and with the name Azure DevOps, it is VSTS, TFS and Azure all jelled together to form a set of tools.

I hope this has been informative and thank you for reading!

Comments

Popular posts from this blog

Microsoft Azure AI Solution - AI-100

  I have curated a list of articles from Microsoft documentation for each objective of the AI-100 exam AI-100 Azure AI Solution Online Course Pluralsight (Learning Path) Microsoft Azure AI Engineer Certification [Free Trial] LinkedIn Learning [Free Trial] Learning Microsoft Cognitive Services for Developers Whizlabs Microsoft Azure Exam AI-100 Certification Udemy ML and AI using Microsoft Cognitive Services AI-100 Azure AI Practice Test and Lab Udemy Practice Test                Designing and Implementing an Azure AI Exam AI-100 GitHub Labs Labs resources on GitHub AI-100 Azure AI Related Study Materials Coursera                                    Deep Learning Specialization by Andrew Ng Amazon e-book (PDF) Learning Microsoft Cognitive Services To view other Azure Certificate Study Guides, click here  I hope this has been inf...

Microsoft Cloud Adoption Framework

The Cloud Adoption Framework for Azure is a collection of documentation, technical guidance, best practices, and tools that aid in aligning business, organizational readiness, and technology strategies. This alignment enables a clear and actionable journey to the cloud that rapidly delivers on the desired business outcomes. The Cloud Adoption Framework contains detailed information to cover an end-to-end cloud adoption journey. It begins with setting the business strategy, which should align to actionable technology projects that deliver on the desired business outcomes. It then describes how the organization must: Prepare its people with technical readiness. Adjust processes to drive business and technology changes. Enable business outcomes through the implementation of the defined technology plan. Define strategy Organizations adopt the cloud to help drive business transformation, such as processes and product improvement, market growth, and increased profitability. Let’s look at the...

Machine Learning in Azure

Machine learning is a method of data analysis that automates analytical model building. It's a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human interaction. This series of articles intend to elaborate the usage of Azure Machine learning and usage of different machine learning tools in Azure ML studio. This is the first post and it walks you through the introduction to the Azure ML studio and how to upload data to the tool. Two most widely adopted machine learning methods are,  Supervised learning: algorithms are trained using labeled examples, such as    an input where the desired output is known Unsupervised learning: is used against data that has no historical labels. The System is not told the "right answer." The algorithm must figure out what is being shown.   Differences between data mining, machine learning and deep learning  Data mining is about to ident...