ActiveBatch Workload Automation vs. Apache Airflow

Any large business or organisation that wants to manage their workload effectively and with the least amount of room for error might choose the ActiveBatch Automation tool. Being a consultant I feel that It aids in task automation and has the flexibility to change in response to varying company requirements. It helps to save huge time by doing all the repetitive tasks on daily basis. During the patching activity the schedulers can be stopped. It also help by alerting us if any system/job is down so that SLA can be saved. Overall ActiveBatch Automation stands as a dependable cornerstone for ensuring the seamless operation of our tasks.

Incentivized

For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.

Incentivized

• Businesses can use ActiveBatch to plan tasks based on parameters like frequency, dependencies, and the time of day. By automating typical actions like backups and data transfers, businesses can make sure that crucial operations go off without a hitch.
• Multiple systems and apps can be used in complicated workflows that ActiveBatch can automate. For instance, it can automate a workflow for processing orders from beginning to end, from the customer order through inventory control and delivery through the processing of invoices and payments.
• Files can be sent between many platforms and systems safely with ActiveBatch. Transfers to cloud-based storage systems like Amazon S3 and Microsoft Azure are also included in this. SFTP and FTP transfers are also included.

• In charge of the ETL processes.
• As there is no incoming or outgoing data, we may handle the scheduling of tasks as code and avoid the requirement for monitoring.

Incentivized

• On RARE occasions, have seen scheduling properties changed that don't take effect.
• Simpler to understand/more robust reporting options would be nice to have.
• Maybe I'm missing something, but why doesn't the Instances view show completion time? Just execution time and duration.

Incentivized

• they should bring in some time based scheduling too not only event based
• they do not store the metadata due to which we are not able to analyze the workflows
• they only support python as of now for scripted pipeline writing

Incentivized

We can easily add new plans/jobs in our batch schedules. Also, coordination with reporting and QA jobs is simple to do. Building schedules, restarting jobs, triggering dependencies is easy to understand. The system is very stable and allows us to easily see overall processing times.

Incentivized

Easy to learn Easy to use Robust workflow orchestration framework Good in dependent job management

Incentivized

My colleague contacted them directly, I only know hearsay on this but it was not good.

Incentivized

The workload automation solution is based on the specific needs of an organization, as well as the features, capabilities, and costs of various solutions. A thorough evaluation process and consideration of these factors can help ensure the selection of a solution that aligns with overall business objectives and meets the specific needs of the organization.

Incentivized

There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.

Incentivized

• I have not run numbers to determine hard impact, but a quick estimate is that at least one job is running for a average of about 6 hours per day - that 6 hours, if done by hand, would equate to about 30 - 40 hours per day (and in some cases, could not be duplicated manually, as the job repeats faster than a person could accomplish one cycle.)

• A lot of helpful features out-of-the-box, such as the DAG visualizations and task trees
• Allowed us to implement complex data pipelines easily and at a relatively low cost

Incentivized