Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.
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Jenkins
Score 8.5 out of 10
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Jenkins is an open source automation server. Jenkins provides hundreds of plugins to support building, deploying and automating any project. As an extensible automation server, Jenkins can be used as a simple CI server or turned into a continuous delivery hub for any project.
Using Jenkins and Kafka, it is not for the same purpose, although it might be similar. I would say AirFlow is really what it says on the can - workflow management. For our organisation, the purpose is clear. So long your aim is to have a rich workflow scheduler and job …
For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.
Jenkins is a highly customizable CI/CD tool with excellent community support. One can use Jenkins to build and deploy monolith services to microservices with ease. It can handle multiple "builds" per agent simultaneously, but the process can be resource hungry, and you need some impressive specs server for that. With Jenkins, you can automate almost any task. Also, as it is an open source, we can save a load of money by not spending on enterprise CI/CD tools.
Automated Builds: Jenkins is configured to monitor the version control system for new pull requests. Once a pull request is created, Jenkins automatically triggers a build process. It checks out the code, compiles it, and performs any necessary build steps specified in the configuration.
Unit Testing: Jenkins runs the suite of unit tests defined for the project. These tests verify the functionality of individual components and catch any regressions or errors. If any unit tests fail, Jenkins marks the build as unsuccessful, and the developer is notified to fix the issues.
Code Analysis: Jenkins integrates with code analysis tools like SonarQube or Checkstyle. It analyzes the code for quality, adherence to coding standards, and potential bugs or vulnerabilities. The results are reported back to the developer and the product review team for further inspection.
Jenkins is quite hard to configure and the User Interface is slow and hard to navigate. It takes some time to get to use to the interface and know the "tricks" in order to set up the jobs to your liking. Even if you use a pipeline file you will have to do some configuration and extra steps.
No, when we integrated this with GitHub, it becomes more easy and smart to manage and control our workforce. Our distributed workforce is now streamlined to a single bucket. All of our codes and production outputs are now automatically synced with all the workers. There are many cases when our in-house team makes changes in the release, our remote workers make another release with other environment variables. So it is better to get all of the work in control.
As with all open source solutions, the support can be minimal and the information that you can find online can at times be misleading. Support may be one of the only real downsides to the overall software package. The user community can be helpful and is needed as the product is not the most user-friendly thing we have used.
There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.
Overall, Jenkins is the easiest platform for someone who has no experience to come in and use effectively. We can get a junior engineer into Jenkins, give them access, and point them in the right direction with minimal hand-holding. The competing products I have used (TravisCI/GitLab/Azure) provide other options but can obfuscate the process due to the lack of straightforward simplicity. In other areas (capability, power, customization), Jenkins keeps up with the competition and, in some areas, like customization, exceeds others.
