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AWS Batch

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Beanstalk

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AWS Batch vs Beanstalk: What are the differences?

Introduction

AWS Batch and Beanstalk are both services provided by Amazon Web Services (AWS) that are used to deploy and manage applications in the cloud. While they may have some similarities, there are key differences between the two that make them suited for different use cases.

  1. Deployment and Management: AWS Batch is a fully managed service that is designed for executing batch computing workloads, whereas AWS Elastic Beanstalk is a platform as a service (PaaS) that provides an easy way to deploy and manage applications.

  2. Application Architecture: AWS Batch is designed for running batch computing workloads, which are typically non-interactive and can be parallelized. On the other hand, Elastic Beanstalk is designed for running web applications that are interactive and require a user interface.

  3. Flexibility and Control: AWS Batch offers more flexibility and control over the underlying infrastructure, allowing users to have fine-grained control over the execution environment and infrastructure resources. Elastic Beanstalk, on the other hand, abstracts away the underlying infrastructure and provides a more simplified deployment and management experience.

  4. Scaling and Autoscaling: With AWS Batch, users have more control over scaling and autoscaling of their compute resources, as they can define their own scaling policies based on workload requirements. Elastic Beanstalk, on the other hand, provides built-in autoscaling capabilities that automatically scale resources based on traffic patterns.

  5. Pricing: AWS Batch is priced based on the resources consumed by batch jobs, including compute resources, storage, and data transfer. Elastic Beanstalk is priced based on the underlying resources provisioned for the application, including compute instances, storage, and data transfer.

  6. Integration with Other Services: AWS Batch integrates well with other AWS services, such as Amazon S3 for storing input and output data, and AWS CloudWatch for monitoring batch jobs. Elastic Beanstalk also integrates with various AWS services, such as Amazon RDS for database management and Amazon SES for email notification.

In summary, AWS Batch is designed for batch computing workloads and offers more control and flexibility over the underlying infrastructure, while Elastic Beanstalk is designed for web applications and provides an easy way to deploy and manage applications with built-in autoscaling capabilities.

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Pros of AWS Batch
Pros of Beanstalk
  • 3
    Containerized
  • 3
    Scalable
  • 14
    Ftp deploy
  • 9
    Deployment
  • 8
    Easy to navigate
  • 4
    Code Editing
  • 4
    HipChat Integration
  • 4
    Integrations
  • 3
    Code review
  • 2
    HTML Preview
  • 1
    Security
  • 1
    Blame Tool
  • 1
    Cohesion

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Cons of AWS Batch
Cons of Beanstalk
  • 3
    More overhead than lambda
  • 1
    Image management
    Be the first to leave a con

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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 companies use AWS Batch?
    What companies use Beanstalk?
    See which teams inside your own company are using AWS Batch or Beanstalk.
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    What tools integrate with AWS Batch?
    What tools integrate with Beanstalk?

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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.
    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.
    Azure Functions
    Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
    See all alternatives