Implementation in your environment - 7.2

Talend Software Development Life Cycle Best Practices Guide

Version
7.2
Language
English (United States)
Product
Talend Big Data
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Module
Talend Administration Center
Talend Artifact Repository
Talend CommandLine
Talend JobServer
Talend Studio
Content
Administration and Monitoring
Deployment
Design and Development
The following diagram shows how Talend tools can be used and integrated in your own Java fabric, ensuring quick integration and quality of your projects from the beginning to the end of your software life cycle.
The main phases of the Continuous Integration and Deployment processes that are presented in this diagram are the following:
  • 1 and 2 (Git or Subversion): Version and Revision Control

    Committing: Developers design Jobs, Routes, Services and Tests in Talend Studio and commit them to Git or Subversion.

    Checking out sources: Git and/or SVN are linked to the Continuous Integration server that checks out the artifacts and Tests sources in the form of .item and .properties files as well as the corresponding pom.xml files pregenerated by the CommandLine.

  • 3 to 6 (in external Java factory): Maven Build, Continuous Integration and Deployment

    Generating sources: The Talend CI Builder and Talend CommandLine tools generate the Git/SVN sources and pass them to the Continuous Integration server that is used (Jenkins for example).

    Compiling sources : An automated build is launched on the server to compile sources (transformed to Java classes).

    Testing: Automated builds are launched on the server to execute Tests, and the server dashboard allows you to monitor and audit code quality before packaging.

    Packaging and publishing: Once the Tests are executed and the bugs are fixed, items are packaged and published in an artifact repository (Nexus, Artifactory) in the form of .zip files or a Docker container as a Docker image.

    The versioned release candidate is then deployed to Production.

Continuous Integration and Deployment ensure a quick, effective, automated and safe deployment to Production.