Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Airflow
Score 8.7 out of 10
N/A
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
AWS Batch
Score 7.8 out of 10
N/A
With AWS Batch, users package the code for batch jobs, specify dependencies, and submit batch jobs using the AWS Management Console, CLIs, or SDKs. AWS Batch allows users to specify execution parameters and job dependencies, and facilitates integration with a broad range of popular batch computing workflow engines and languages (e.g., Pegasus WMS, Luigi, Nextflow, Metaflow, Apache Airflow, and AWS Step Functions).N/A
Azure DevOps
Score 8.1 out of 10
N/A
Azure DevOps (formerly VSTS, Microsoft Visual Studio Team System) is an agile development product that is an extension of the Microsoft Visual Studio architecture. Azure DevOps includes software development, collaboration, and reporting capabilities.
$2
per GB (first 2GB free)
Pricing
Apache AirflowAWS BatchAzure DevOps
Editions & Modules
No answers on this topic
No answers on this topic
Azure Artifacts
$2
per GB (first 2GB free)
Basic Plan
$6
per user per month (first 5 users free)
Azure Pipelines - Self-Hosted
$15
per extra parallel job (1 free parallel job with unlimited minutes)
Azure Pipelines - Microsoft Hosted
$40
per parallel job (1,800 minutes free with 1 free parallel job)
Basic + Test Plan
$52
per user per month
Offerings
Pricing Offerings
Apache AirflowAWS BatchAzure DevOps
Free Trial
NoNoNo
Free/Freemium Version
YesNoNo
Premium Consulting/Integration Services
NoNoNo
Entry-level Setup FeeNo setup feeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache AirflowAWS BatchAzure DevOps
Considered Multiple Products
Apache Airflow
Chose Apache Airflow
Airflow was best suited in my use case for designing the ETL pipelines in a scripted manner for workflows & the UI was very good & easy to use.
AWS Batch

No answer on this topic

Azure DevOps

No answer on this topic

Features
Apache AirflowAWS BatchAzure DevOps
Workload Automation
Comparison of Workload Automation features of Product A and Product B
Apache Airflow
8.7
12 Ratings
5% above category average
AWS Batch
7.3
7 Ratings
13% below category average
Azure DevOps
-
Ratings
Multi-platform scheduling9.312 Ratings6.06 Ratings00 Ratings
Central monitoring8.912 Ratings8.06 Ratings00 Ratings
Logging8.612 Ratings10.06 Ratings00 Ratings
Alerts and notifications9.312 Ratings5.06 Ratings00 Ratings
Analysis and visualization6.712 Ratings5.95 Ratings00 Ratings
Application integration9.412 Ratings8.76 Ratings00 Ratings
Best Alternatives
Apache AirflowAWS BatchAzure DevOps
Small Businesses

No answers on this topic

No answers on this topic

GitHub
GitHub
Score 9.1 out of 10
Medium-sized Companies
ActiveBatch Workload Automation
ActiveBatch Workload Automation
Score 7.5 out of 10
Apache Airflow
Apache Airflow
Score 8.7 out of 10
GitHub
GitHub
Score 9.1 out of 10
Enterprises
Control-M
Control-M
Score 9.3 out of 10
Control-M
Control-M
Score 9.3 out of 10
Perforce P4
Perforce P4
Score 7.2 out of 10
All AlternativesView all alternativesView all alternativesView all alternatives
User Ratings
Apache AirflowAWS BatchAzure DevOps
Likelihood to Recommend
8.8
(12 ratings)
5.0
(7 ratings)
8.4
(69 ratings)
Likelihood to Renew
-
(0 ratings)
-
(0 ratings)
10.0
(3 ratings)
Usability
8.2
(3 ratings)
8.0
(1 ratings)
7.8
(9 ratings)
Support Rating
-
(0 ratings)
-
(0 ratings)
8.1
(11 ratings)
Implementation Rating
-
(0 ratings)
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Apache AirflowAWS BatchAzure DevOps
Likelihood to Recommend
Apache
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.
Read full review
Amazon AWS
More appropriate if you have a tech group that can use more of the AWS Batch rather than one or 2 things. It works great for me, but there was a huge learning curve the first week of using it. Now, I love it - and I hope to dig deep into other parts not just S3.
Read full review
Microsoft
Azure DevOps works well when you’ve got larger delivery efforts with multiple teams and a lot of moving parts, and you need one place to plan work, track it properly, and see how everything links together. It’s especially useful when delivery and development are closely tied and you want backlog items, code and releases connected rather than spread across tools. Where it’s less of a fit is for small teams or simple pieces of work, as it can feel like more setup and process than you really need, and non-technical users often struggle with the interface. It also isn’t great if you want instant, easy programme-level views or a very visual planning experience without putting time into configuration.
Read full review
Pros
Apache
  • 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.
Read full review
Amazon AWS
  • Easy to orchestrate and trigger jobs
  • No time limit issues like lambda
  • Multiple Jobs can be run in same single compute and job queue
  • JOb queue can queue up task for parralled or serialization
Read full review
Microsoft
  • Utilize Git as a repository to share work between multiple users
  • Ability to configure Pipelines to build containers to run virtual deployments and testing scripts.
  • Split individual tasks and relate to master documents for quick navigation and ability to see overall picture of project.
  • Track status of each task
  • Integrate with Git to utilize branches, merging, approvals, history, etc.
Read full review
Cons
Apache
  • 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.
Read full review
Amazon AWS
  • Jobs monitoring dashboards are not matured
  • Documentation and support is something which can be improved
  • Sometime i faced the slow response or slow in performance i would say
Read full review
Microsoft
  • I did mention it has good visibility in terms of linking, but sometimes items do get lost, so if there was a better way to manage that, that would be great.
  • The wiki is not the prettiest thing to look at, so it could have refinements there.
  • It could improve the search slightly better.
Read full review
Likelihood to Renew
Apache
No answers on this topic
Amazon AWS
No answers on this topic
Microsoft
I don't think our organization will stray from using VSTS/TFS as we are now looking to upgrade to the 2012 version. Since our business is software development and we want to meet the requirements of CMMI to deliver consistent and high quality software, this SDLC management tool is here to stay. In addition, our company uses a lot of Microsoft products, such as Office 365, Asp.net, etc, and since VSTS/TFS has proved itself invaluable to our own processes and is within the Microsoft family of products, we will continue to use VSTS/TFS for a long, long time.
Read full review
Usability
Apache
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.
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Amazon AWS
Key advantages include cost-effectiveness through dynamic resource provisioning and the use of spot instances. It auto-scales to meet workload demands, allowing easy job submission via the AWS Management Console or SDKs. It integrates seamlessly with other services like S3 and CloudWatch. It features automatic retries for failed jobs. It allows for a custom computing environment tailored to specific needs
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Microsoft
It's a great help to get more information about new feature release and stay updated on what the dev team is working on. I like how easy it is to just login and read through the work items. Each work item has basic details: Title, Description, Assigned to, State, Area (what it belongs to), and iteration (when it’s worked on). See image above.They move through different states (New → Discovery → Ready for Prod → etc.).
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Support Rating
Apache
No answers on this topic
Amazon AWS
No answers on this topic
Microsoft
When we've had issues, both Microsoft support and the user community have been very responsive. DevOps has an active developer community and frankly, you can find most of your questions already asked and answered there. Microsoft also does a better job than most software vendors I've worked with creating detailed and frequently updated documentation.
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Implementation Rating
Apache
No answers on this topic
Amazon AWS
No answers on this topic
Microsoft
Was not part of the process.
Read full review
Alternatives Considered
Apache
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.
Read full review
Amazon AWS
We wanted to start everything on a scale & with fewer resources to manage the underlying infrastructure.
Read full review
Microsoft
Microsoft Planner is used by project managers and IT service managers across our organization for task tracking and running their team meetings. Azure DevOps works better than Planner for software development teams but might possibly be too complex for non-software teams or more business-focused projects. We also use ServiceNow for IT service management and this tool provides better analysis and tracking of IT incidents, as Azure DevOps is more suited to development and project work for dev teams.
Read full review
Return on Investment
Apache
  • 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
  • Donot use it if you have very less usecases
Read full review
Amazon AWS
  • Overall over business is able to save the cost
  • Saved our times to improve the existing process
  • Able to integrate with other applications as well, so that is plus point
Read full review
Microsoft
  • We have saved a ton of time not calculating metrics by hand.
  • We no longer spend time writing out cards during planning, it goes straight to the board.
  • We no longer track separate documents to track overall department goals. We were able to create customized icons at the department level that lets us track each team's progress against our dept goals.
Read full review
ScreenShots