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

• Makes scheduling easy to understand, follow, and rerun jobs when necessary.
• Allows for cross-team coordination of scheduled tasks which reduces errors.
• Makes stopping jobs easy when needed for server/database downtime.
• Scripting enables us to easily change email addresses for failed job alerts.
• Nested plans/jobs make creating and changing dependencies simple.

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

• String handling / parsing. I find myself using PowerShell to do a fair amount of text parsing (particularly if manipulations are needed) - not necessarily a bad thing, but certainly a place where ActiveBatch could be improved.
• Debugging - or lack of it! With no stepping debugger, it can be a longer process than many other programming / scripting environments: rather than simply stepping through and observing state changes, I find myself inserting logging steps to excess, then having to clean them up once the error is found.
• The perennial - Documentation! While a near-universal complaint for *any* software, ActiveBatch's developer documentation is somewhat spotty - just where I need detail, I find summary-level info. There is lots of documentation (as there should be for a tool with such a wide range of applications), but it is in mixed formats (some PDF, some CHM), and the descriptions of specific fields within job steps is often little more than I can get in a tool-tip in the GUI. Allowable ranges, expected formats for string data, and similar helpful details are inconsistent.
• The KnowledgeBase at ASCI's web site often has examples which answer the questions I have, but not always - and not always under the search terms one would think to use.

• A local "dry run" or IDE plugin that can validate and simulate DAG execution without needing a full environment.
• Better feedback on DAG parse errors in the UI or CLI.
• Navigating large DAGs with hundreds of tasks can be slow and hard to understand visually.

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

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

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

Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.

Incentivized

• ActiveBatch can automate intricate procedures and minimise manual involvement, which can boost an organization's production and efficiency.
• Organisations can save money by using ActiveBatch to automate operations, which lowers the expenses of manual labour and potential mistakes.
• Implementing ActiveBatch could come with hefty up-front expenses including licencing, instruction, and consultancy fees, which could have a short-term negative impact on ROI.

• Most of the ETL processes were automated, cutting down on human labor.
• Apache Airflow's user interface (UI) was very informative and straightforward.
• Since ETL processes were providing data via airflow, we were able to gain a deeper comprehension of the data at hand.

Incentivized