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AWS Data Pipeline

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Postman

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Pros of AWS Data Pipeline
Pros of Postman
  • 1
    Easy to create DAG and execute it
  • 490
    Easy to use
  • 369
    Great tool
  • 276
    Makes developing rest api's easy peasy
  • 156
    Easy setup, looks good
  • 144
    The best api workflow out there
  • 53
    It's the best
  • 53
    History feature
  • 44
    Adds real value to my workflow
  • 43
    Great interface that magically predicts your needs
  • 35
    The best in class app
  • 12
    Can save and share script
  • 10
    Fully featured without looking cluttered
  • 8
    Collections
  • 8
    Option to run scrips
  • 8
    Global/Environment Variables
  • 7
    Shareable Collections
  • 7
    Dead simple and useful. Excellent
  • 7
    Dark theme easy on the eyes
  • 6
    Awesome customer support
  • 6
    Great integration with newman
  • 5
    Documentation
  • 5
    Simple
  • 5
    The test script is useful
  • 4
    Saves responses
  • 4
    This has simplified my testing significantly
  • 4
    Makes testing API's as easy as 1,2,3
  • 4
    Easy as pie
  • 3
    API-network
  • 3
    I'd recommend it to everyone who works with apis
  • 3
    Mocking API calls with predefined response
  • 2
    Now supports GraphQL
  • 2
    Postman Runner CI Integration
  • 2
    Easy to setup, test and provides test storage
  • 2
    Continuous integration using newman
  • 2
    Pre-request Script and Test attributes are invaluable
  • 2
    Runner
  • 2
    Graph
  • 1
    <a href="http://fixbit.com/">useful tool</a>

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Cons of AWS Data Pipeline
Cons of Postman
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    • 10
      Stores credentials in HTTP
    • 9
      Bloated features and UI
    • 8
      Cumbersome to switch authentication tokens
    • 7
      Poor GraphQL support
    • 5
      Expensive
    • 3
      Not free after 5 users
    • 3
      Can't prompt for per-request variables
    • 1
      Import swagger
    • 1
      Support websocket
    • 1
      Import curl

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    What is AWS Data Pipeline?

    AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.

    What is Postman?

    It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide.

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    What companies use AWS Data Pipeline?
    What companies use Postman?
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    What tools integrate with AWS Data Pipeline?
    What tools integrate with Postman?

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    What are some alternatives to AWS Data Pipeline and Postman?
    AWS Glue
    A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
    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.
    AWS Step Functions
    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
    Apache NiFi
    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
    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.
    See all alternatives