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 8.3 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 …
Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
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.
Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.
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.
UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
We have a certain buy-in as we have made a lot of integrations and useful tools around jenkins, so it would cost us quite some time to change to another tool. Besides that, it is very versatile, and once you have things set up, it feels unnecessary to change tool. It is also a plus that it is open source.
For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.
Jenkins streamlines development and provides end to end automated integration and deployment. It even supports Docker and Kubernetes using which container instances can be managed effectively. It is easy to add documentation and apply role based access to files and services using Jenkins giving full control to the users. Any deviation can be easily tracked using the audit logs.
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.
It is worth well the time to setup Jenkins in a docker container. It is also well worth to take the time to move any "Jenkins configuration" into Jenkinsfiles and not take shortcuts.
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.
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.
Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
