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AWS Batch

90
250
+ 1
6
Zappa

62
99
+ 1
0
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AWS Batch vs Zappa: What are the differences?

Introduction

  1. Cost Model: AWS Batch follows a pay-as-you-go model where you only pay for the compute resources you use, while Zappa has a free tier for small applications but charges for higher usage levels, making it more suitable for smaller projects with limited budget.

  2. Integration with AWS Services: AWS Batch integrates seamlessly with other AWS services such as S3, DynamoDB, and more, providing a comprehensive cloud computing environment, whereas Zappa is specifically designed for serverless deployments on AWS Lambda, offering a more streamlined integration with Lambda functions only.

  3. Scalability and Flexibility: AWS Batch offers high flexibility and scalability for running parallel workloads and managing computing resources efficiently, making it ideal for handling large-scale batch processing tasks, whereas Zappa is focused on creating and deploying serverless web applications quickly, limiting its scalability capabilities compared to AWS Batch.

  4. Customization and Control: AWS Batch provides advanced customization options and fine-grained control over job scheduling, priority, and resource allocation, making it suitable for diverse batch processing requirements, whereas Zappa simplifies the deployment process, sacrificing some level of control and customization in favor of rapid development and deployment.

  5. Maintenance and Monitoring: AWS Batch includes built-in monitoring tools and automatic scaling features to optimize resource utilization and monitor job progress, enabling easy management of batch workloads, while Zappa requires manual monitoring and maintenance of serverless applications, which may require additional effort from the user to ensure optimal performance.

  6. Community Support and Documentation: AWS Batch benefits from extensive documentation, tutorials, and a large community of users and developers, providing a wealth of resources for troubleshooting and learning, whereas Zappa, being a more specialized tool, may have a smaller user base and limited community support, potentially requiring more independent problem-solving skills from users.

In Summary, AWS Batch and Zappa differ significantly in cost models, integration capabilities, scalability, customization, maintenance, and community support, making them suitable for different use cases based on specific project requirements.

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Pros of AWS Batch
Pros of Zappa
  • 3
    Containerized
  • 3
    Scalable
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    Cons of AWS Batch
    Cons of Zappa
    • 3
      More overhead than lambda
    • 1
      Image management
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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 Zappa?

      Zappa makes it super easy to deploy all Python WSGI applications on AWS Lambda + API Gateway. Think of it as "serverless" web hosting for your Python web apps. That means infinite scaling, zero downtime, zero maintenance - and at a fraction of the cost of your current deployments!

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      What companies use AWS Batch?
      What companies use Zappa?
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      What tools integrate with AWS Batch?
      What tools integrate with Zappa?

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      What are some alternatives to AWS Batch and Zappa?
      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.
      NGINX
      nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. According to Netcraft nginx served or proxied 30.46% of the top million busiest sites in Jan 2018.
      See all alternatives