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AWS SDK for Python cross-service examples

python/cross_service/README.md

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AWS SDK for Python cross-service examples

Overview

This README lists the cross-service examples available for the AWS SDK for Python (Boto3). Each folder in this directory contains the following cross-service examples. A README in each folder describes how to run the example.

A cross-service example is an application that works across multiple AWS services using the AWS SDK for Python.

⚠️ Important

  • Running this code might result in charges to your AWS account.
  • Running the tests might result in charges to your AWS account.
  • We recommend that you grant your code least privilege. At most, grant only the minimum permissions required to perform the task. For more information, see Grant least privilege.
  • This code is not tested in every AWS Region. For more information, see AWS Regional Services.

Cross-service examples

  • AWS Chalice and AWS Lambda REST API example

    Shows how to use AWS Chalice to create a serverless REST API that uses Amazon API Gateway, AWS Lambda, and Amazon DynamoDB. The REST API simulates a system that tracks daily cases of COVID-19 in the United States, using fictional data.

    • Amazon API Gateway
    • AWS CloudFormation
    • Amazon DynamoDB
    • AWS Lambda

  • Amazon API Gateway websocket chat example

    Shows how to use Amazon API Gateway V2 to create a websocket API that integrates with AWS Lambda and Amazon DynamoDB.

    • Amazon API Gateway
    • Amazon DynamoDB
    • AWS Lambda

  • Track work items in an Aurora Serverless database

    Shows how to create a REST service that lets you store work items in an Amazon Aurora Serverless database and use Amazon Simple Email Service (Amazon SES) to send email reports of work items.

    • Aurora
    • Amazon SES

  • Amazon Aurora Serverless REST API lending library example

    Shows how to use the Amazon Relational Database Service (Amazon RDS) API and AWS Chalice to create a REST API backed by an Amazon Aurora database. The web service is fully serverless and represents a simple lending library where patrons can borrow and return books.

    • Amazon API Gateway
    • AWS Lambda
    • Amazon RDS
    • AWS Secrets Manager

  • Track work items in a DynamoDB table

    Shows how to create a REST service that lets you store work items in an Amazon DynamoDB table and use Amazon Simple Email Service (Amazon SES) to send email reports of work items.

    • Amazon DynamoDB
    • Amazon SES

  • Analyzing photos using Amazon Rekognition

    Shows you how to create a web application that lets you upload photos to an Amazon Simple Storage Service (Amazon S3) bucket, use Amazon Rekognition to analyze and label the photos, and use Amazon Simple Email Service (Amazon SES) to send email reports of image analysis.

    • Amazon Rekognition
    • Amazon S3
    • Amazon SES

  • Moderate content using Amazon Rekognition with URL support

    Shows you how to create a Lambda function that analyzes images with Amazon Rekognition to moderate content. The Lambda function is invoked by API Gateway so that you can POST content to the API Gateway URL to receive moderation data.

    • Amazon API Gateway
    • AWS CloudFormation
    • AWS Lambda
    • Amazon Rekognition

  • AWS Step Functions messenger example

    Shows how to use AWS Step Functions to create and run a state machine that retrieves message records from Amazon DynamoDB and sends messages to an Amazon Simple Queue Service (Amazon SQS) queue.

    • AWS CloudFormation
    • Amazon DynamoDB
    • AWS Lambda
    • Amazon SQS
    • AWS Step Functions

  • Detect entities in extracted text using a Jupyter notebook

    Shows how to use a Jupyter notebook to detect entities in text that is extracted from an image. This example uses Amazon Textract to extract text from an image stored in Amazon Simple Storage Service (Amazon S3) and Amazon Comprehend to detect entities in the extracted text.

    • Amazon Comprehend
    • Amazon S3
    • Amazon Textract

  • Amazon Textract explorer example

    Shows how to use Amazon Textract to detect text, form, and table elements in a document image. The input image and Textract output are shown in a Tkinter application that lets you explore the detected elements. The application starts asynchronous jobs, publishes notifications to an Amazon Simple Notification Service (Amazon SNS) topic when the job completes, and polls an Amazon Simple Queue Service (Amazon SQS) queue for a job completion message and displays the results.

    • Amazon S3
    • Amazon SNS
    • Amazon SQS
    • Amazon Textract

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SPDX-License-Identifier: Apache-2.0