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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Bitbucket
Score 8.5 out of 10
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Bitbucket is a Git repository and code collaboration platform, featuring automated testing and code deployment. Bitbucket Cloud Premium provides AI-powered development, more granular access controls, and enforced code quality, and Bitbucket Data Center provides a self-hosted option.
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.
As a team we need to push code into the repo on daily basis, Bitbucket has proven that is a reliable and secure server to save and get the code available in no time. The administration part is really easy and there's an extra tool for every developer profile either if you want to use the console or a GUI like Sourcetree.
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.
Very easy to integrate with other DevOps tools like Jenkins and with project/workflow management tools like JIRA.
Very efficient in managing security and compliance standards for code, especially during pull requests, merge requests, branching, etc.
Very robust in performance, especially the cloud and datacenter versions hardly hit any performance issues and supports more than 2000+ developers in my company.
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.
All products have room for improvement. The system improves over time with better and better integrations and I look forward to even more features without paying extra! The system has increased transparency across my organization and with this transparency comes increased throughput on projects. I don't think I can go back to any other system and we are definitely married to this product.
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.
The architecture of Bitbucket makes it more easily scalable than other source code management repositories. Also, administration and maintaining the instance is very easy. It integrates with JIRA and other CI/CD applications which makes it more useful to reduce the efforts. It supports multiple plugins and those bring a lot of extra functionality. It increases the overall efficiency and usefulness of Bitbucket.
The customer support provided by Atlassian (Bitbucket's parent company that also makes Jira, Confluence, etc.) is very helpful. They seem to be very concerned about any issues reported with their products and even just questions about functionality. They are constantly improving the products with new features in nearly every release. Plus they have a plethora of online documentation to reference.
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.
For the features we were looking at, Bitbucket, GitHub and GitLab were all at par and were in a similar price range. We found that GitHub was the most full featured should we need to scale very quickly. GitLab was at par with GitHub for our future needs, but GitHub was a more familiar tool compared to GitLab. Bitbucket won out because of its close integration with Jira and being in the Atlassian family. It was also cheaper than GitHub. As we started with Jira, Bitbucket addition became a natural next step for us. We really liked Bitbucket and stayed with it but we do know we have great options in the form of GitHub and GitLab should we need to scale fast.
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
