AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems.
There is no additional charge for AWS Batch. You only pay for the AWS resources (e.g. EC2 instances or Fargate jobs) you create to store and run your batch jobs. From the AWS Batch use cases page, we can see an example similar to this scenario wherein Digital Media and Entertainment companies require highly scalable batch computing resources to enable accelerated and automated processing of data as well as the compilation and processing of files, graphics, and visual effects for high-resolution video content. Use AWS Batch to accelerate content creation, dynamically scale media packaging, and automate asynchronous media supply chain workflows.
In AWS Batch, job queues are mapped to one or more compute environments. Compute environments contain the Amazon ECS container instances that are used to run containerized batch jobs. A specific compute environment can also be mapped to one or many job queues. Within a job queue, the associated compute environments each have an order that's used by the scheduler to determine where jobs that are ready to be run should run.