AWS Batch excels in scalability and parallelism, handling workloads from small to extensive. Users value the flexibility to customize compute and memory requirements. It integrates seamlessly with EC2, S3, Lambda, Step Functions, and supports Docker containers. Templates streamline configuration, while security features like IAM roles ensure controlled access. It's praised for cost-effectiveness, especially with Fargate, enhancing efficiency by eliminating dependencies on EC2 instances, and offering massive scaling with minimal setup.
- "AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
- "We can easily integrate AWS container images into the product."
- "There is one other feature in confirmation or call confirmation where you can have templates of what you want to do and just modify those to customize it to your needs. And these templates basically make it a lot easier for you to get started."
AWS Batch faces challenges with cost-effectiveness, documentation, UI glitches, and integration with other AWS services, causing difficulties for junior developers. Improved pricing, error handling for Spot Instances, cold start issues, and Fargate startup times are needed. Users suggest enhancements in deployment, logging, job monitoring, and IAM privilege setup. Faster log displays, advanced error handling, and better GUI descriptions would benefit technical and non-technical users. Scalability, reliability, and dynamic resource allocation require further development.
- "The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources."
- "When we run a lot of batch jobs, the UI must show the history."
- "The main drawback to using AWS Batch would be the cost. It will be more expensive in some cases than using an HPC. It's more amenable to cases where you have spot requirements."