Apache Airflow vs. AWS Batch vs. Broadcom Automic Automation

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.

Incentivized

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.

Incentivized

This product has huge capacity to run millions of jobs per month, can serve to enterprise level of business. If client is looking for platform base solution which can be deployed on-prem or cloud base technologies then this solution works well. It has very good licensing mechanism which is based on number of agents, and there is no limitation of number of jobs run. So client has not to worry on limitation of jobs and can scale of the automation in the environment. This reduced the total cost of ownership of product. Self service automation feature can reduce the client automation cost significantly. it has good batch job coverages from Mainframe to Microservices.

Incentivized

• 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.

Incentivized

• 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

Incentivized

• Wide range of internal functions
• Many interfaces (incoming and outgoing)
• Modern, web-based user interface
• Strong roadmap with topics such as containerisation and AI
• High throughput possible (> 1 million executions per day)

• 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.

Incentivized

• 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

Incentivized

• Could improve the framework.
• Could improve the underlying coding standards.

Incentivized

Automic is a very strong Automation Platform which enable us to implement user requirements quickly.

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.

Incentivized

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

Incentivized

I have not used Automic One Automation Platform (formerly CA Automic Automation Platform) support.

Incentivized

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.

Incentivized

We wanted to start everything on a scale & with fewer resources to manage the underlying infrastructure.

Incentivized

1. Cost was the major factor without compromising the automation service which this product delivered. Other products are expensive and deliver the same outcome. 2. Limitation of job run, Broadcom Automic Automation allows to run unlimited jobs and helps to increase automation capability. 3. Number of automation packs available with no extra license cost. 4. Depth of coverage of technologies for batch job automation. 5. Required less number of hardware to achieve high availability.

Incentivized

• 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

Incentivized

• 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

Incentivized

• Improved SAP automation thereby giving good returns.
• Provides mail integration which further widens automation scope and profits.

Incentivized