A Scheduler You Can Trust
Use Cases and Deployment Scope
AutoSys Workload Automation is very easy to use for scheduling and monitoring batch jobs over multiple applications and platforms and also over the cloud as well. All our ETL pipelines in Informatica, Azure Data factory, SQL jobs are triggered using AutoSys Workload Automation. Emails are triggered in case of any job delays or failures so that our incident response time is improved . Before automation, batch jobs are required to run manually but AutoSys Workload Automation has reduced the human intervention to run the jobs.
Pros
- 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.
Cons
- 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
Return on Investment
- 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.
Usability
Alternatives Considered
Apache Airflow, Control-M and Azure Data Factory
Other Software Used
Control-M, Apache Airflow, Azure Data Factory
