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AWS Batch
AWS Batch

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Beanstalk
Beanstalk

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AWS Batch vs Beanstalk: What are the differences?

Developers describe AWS Batch as "Fully Managed Batch Processing at Any Scale". 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. On the other hand, Beanstalk is detailed as "Private code hosting for teams". A single process to commit code, review with the team, and deploy the final result to your customers.

AWS Batch can be classified as a tool in the "Serverless / Task Processing" category, while Beanstalk is grouped under "Code Collaboration & Version Control".

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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.

What is Beanstalk?

A single process to commit code, review with the team, and deploy the final result to your customers.
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          What are some alternatives to AWS Batch and Beanstalk?
          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.
          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.
          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.
          Cloud Functions for Firebase
          Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.
          Google Cloud Functions
          Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running
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