Broadcom offers AutoSys Workload Automation, a solution to enhance visibility and control of complex workloads across platforms, ERP systems, and the cloud. It helps to reduce the cost and complexity of managing mission critical business processes, ensuring consistent and reliable service delivery. It is based on the former CA AutoSys, acquired by Broadcom with CA Technologies.
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Azure Batch
Score 8.8 out of 10
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Azure Batch is cloud-scale job scheduling and compute management.
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Pricing
AutoSys Workload Automation
Azure Batch
Editions & Modules
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Offerings
Pricing Offerings
AutoSys Workload Automation
Azure Batch
Free Trial
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No
Free/Freemium Version
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Premium Consulting/Integration Services
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Entry-level Setup Fee
No setup fee
No setup fee
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Features
AutoSys Workload Automation
Azure Batch
Workload Automation
Comparison of Workload Automation features of Product A and Product B
In Informatica or Spark ETL jobs loading data into a Data Warehouse using AutoSys Workload Automation triggers ETL pipelines, monitors completion of jobs and also triggers the power bi report refresh. AutoSys Workload Automation can schedule jobs based on file arrival, and provide alerts if any data loads get failed. A small data team managing greater than 100 jobs. Buying and maintaining AutoSys Workload Automation is overkill in terms of cost, complexity and maintenance does not justify the value
To better serve their consumers, businesses that often interact with those clients who rely on Microsoft's software products may consider migrating to Azure. This program would be useful in any installation of a Microsoft product or suite that necessitates a test of the target environment. It is simple to maintain and implement, making it an ideal IT backbone. If a client doesn't have any use for this particular instrument, it's not going to be of any benefit to them.
In AutoSys Workload Automation, Workflow and job dependencies are shown in a static way, which makes user difficult to visualize more complex job chains or debug failures in a graphical view.
Latest schedulers (like Control-M, Airflow) allow more easy workflow design makes user to understand it better, but AutoSys Workload Automation relies heavily on JIL scripts and text-based job definitions.
AutoSys Workload Automation interface is very slow when searching or filtering over thousand of jobs
AutoSys Workload Automation is a place to schedule, monitor, and manage thousands of jobs. Time, file arrival, dependencies are much very flexible. Once jobs are set up they run consistently with minimal intervention. AutoSys Workload Automation is powerful but very complex o understand. Mainly for beginners the interface is not user friendly. AutoSys Workload Automation is very useful in terms of job scheduling and automation. AutoSys Workload Automation is also useful for strong logging and reporting purpose. AutoSys Workload Automation has reduced a lot of human efforts and manual processing.
In AutoSys Workload Automation, strong legacy presence, proven reliability, slightly simpler for basic scheduling if user already have in-house AutoSys Workload Automation expertise. so we already had investments in AutoSys Workload Automation, trained team, and large numbers of jobs running thus making AutoSys Workload Automation more cost-effective to continue rather than migrate to use AutoSys Workload Automation over Control-M. All our workloads are heavily batch-driven (SAP, ETL) real-time pipelines. AutoSys Workload Automation provides the robustness and enterprise support that Airflow lacks without heavy internal engineering overhead.
They both are great tools and provide the services they have implemented. They are two competing companies that have different cultures and forward mission agendas. I would say Azure is a little easier to support through their user interface for the IT support side of things. Both tools are useful and have their own strength and weakness. If you're a dynamic company with a multitude of customers then both are a required tool to have.
Critical job process consistently complete on time without delays
Significant reduction in team effort due to this resources for available for higher-value tasks. Human intervention is reduced due to this incident costs are also reduced.
Faster incident resolution, improved productivity, and lower downtime.