Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
N/A
Buddy
Score 9.0 out of 10
N/A
Buddy (formerly Springloops) is a SVN/Git source code management tool focused on web development teams. It allows users to code in parallel and share code safely concentrating on results, not on lost changes or overwritten files. With quick deployments, users get rapid collaboration in protected space.
$75
per month
Pricing
Apache Airflow
Buddy
Editions & Modules
No answers on this topic
Free (for freelancers)
$0
per month
On-premises (for teams)
$35
per month per user
Pro (for teams)
$75
per month
Hyper (for teams)
$200
per month
Offerings
Pricing Offerings
Apache Airflow
Buddy
Free Trial
No
No
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Apache Airflow
Buddy
Features
Apache Airflow
Buddy
Workload Automation
Comparison of Workload Automation features of Product A and Product B
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.
Springloops is the best tool for deployment to any environment. Especially, the auto-deployment feature on development servers is essential for the early stages of development. The built-in source control mechanisms are a perfect combination of ease of use and a rich feature set that allows the development team to have an easier and more complete view of each part of the project. A section that is lacking is time tracking - but then this is not the main usage of the service.
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.
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.
Apart from being a great versioning control system Springloops offers the options to automatically deploy code to multiple systems. This feature alone is a determining factor to renew Springloops over and over again. Another important factor is that it offers a full set of tools that help the team during the development cycle. No switching between time-tracking to project management. This is a real time-saver.
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.
Easy to use, automatic deployments, comments on projects are only a few factors. Multiple servers per project is another must-have feature. User permissions and rights offer granular control on access to the system
I rarely use it but when I need it the team is there. During the initial steps of Springloops, I had close contact with one of the founders. He provided support to me over Skype! He didn't have to but he did. We had a couple of long talks about some issues I was facing. He has there regardless of time. It was a great experience
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.
Springloops has a built-in feature that is lacking from Bitbucket (at least on the out-of-the-box functionality). Deployment of projects to various servers/development stages. The process is so easy and painless that even remote servers can act as local environments. This is a feature that differentiates Springloops from other solutions that require other tools to perform the same task.
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
