AutoSys Workload Automation vs. AWS Batch

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

Incentivized

Start with reviewing all your jobs, consolidating the timeline, and making a plan on what all jobs need to be prioritized. These can be optimized based on cost and performance. With a continuously changing workload, AWS Batch helps to build and automate the jobs across various resources. It also helps to manage the performance and workload.

Incentivized

• AutoSys Workload Automation is our scheduler that automates and monitors workflows across all our system reducing manual runs
• Applications which run over multiples systems(cloud, on premise) are coordinated using AutoSys Workload Automation
• AutoSys Workload Automation clustering or failover ensures that workloads continue without any issue.
• AutoSys Workload Automation maintains execution logs and job history, which helps in compliance, audit trails, and root-cause analysis.

Incentivized

• S3 amount of files
• Easy to share files to other sites
• Like that the files and folders can be open and public or completely private
• I like that you can have double or mirroring in different Data Centers for Data Recovery

Incentivized

• 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

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

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.

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

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.

Incentivized

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Incentivized

• 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.

Incentivized

• Well compared to the cost of having multiple lambda's or some other custom solution working, it has has a postiive impact
• Helped in orchestrating the jobs, has saved time of developers

Incentivized