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What is AWS Batch?

It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It 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.
AWS Batch is a tool in the Serverless / Task Processing category of a tech stack.

Who uses AWS Batch?

Companies
29 companies reportedly use AWS Batch in their tech stacks, including FindHotel, fcstack, and OpenGamma.

Developers
37 developers on StackShare have stated that they use AWS Batch.

AWS Batch Integrations

Pros of AWS Batch
2
Scalable
2
Containerized
Decisions about AWS Batch

Here are some stack decisions, common use cases and reviews by companies and developers who chose AWS Batch in their tech stack.

Sumit Singh Chauhan
Data Scientist at Entropik · | 6 upvotes · 10.5K views

I have started using AWS Batch for some long ML inference jobs. So far it's working well and giving a decent performance. Since it is fully managed, it saves a lot of extra work as well. But Batch takes a good amount of time to create a new cluster and then load the job based on the priority of the queue. Going forward would love to put effort into something which is fast to start and give more flexibility as well. What other tools you would suggest for long-running backend jobs which can scale well. I am not looking for something fully managed so ignore the options similar to batch in Google Cloud Platform or Microsoft Azure, Looking for open-source alternatives here. Do you think Kubernetes, RabbitMQ/Kafka will be a good fit or just overkill for my problem. Usually w we get 1000s of requests in parallel and each job might take 20-30 mins in a 2 vCPU system.

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Shared insights
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AWS BatchAWS Batch

We have some use cases for which we do bulk activities on a selected customer base. We query customer base from RDS instance based on specific criteria and need to trigger a particular operation for each customer (e.g. send an email, execute a web function). All bulk activities need to be scheduled at a particular frequency. We are exploring the right stack for the same. Is AWS Batch fit for such a scenario? Where should the customer base be stored? What all applications can be useful? Please suggest.

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AWS Batch Alternatives & Comparisons

What are some alternatives to AWS Batch?
AWS Lambda
AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.
Beanstalk
A single process to commit code, review with the team, and deploy the final result to your customers.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Kubernetes
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.
Serverless
Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.
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AWS Batch's Followers
196 developers follow AWS Batch to keep up with related blogs and decisions.