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Learn AWS Serverless Computing

Learn AWS Serverless Computing

By : Scott Patterson
4 (5)
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Learn AWS Serverless Computing

Learn AWS Serverless Computing

4 (5)
By: Scott Patterson

Overview of this book

Serverless computing is a way to run your code without having to provision or manage servers. Amazon Web Services provides serverless services that you can use to build and deploy cloud-native applications. Starting with the basics of AWS Lambda, this book takes you through combining Lambda with other services from AWS, such as Amazon API Gateway, Amazon DynamoDB, and Amazon Step Functions. You’ll learn how to write, run, and test Lambda functions using examples in Node.js, Java, Python, and C# before you move on to developing and deploying serverless APIs efficiently using the Serverless Framework. In the concluding chapters, you’ll discover tips and best practices for leveraging Serverless Framework to increase your development productivity. By the end of this book, you’ll have become well-versed in building, securing, and running serverless applications using Amazon API Gateway and AWS Lambda without having to manage any servers.
Table of Contents (20 chapters)
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1
Section 1: Why We're Here
4
Section 2: Getting Started with AWS Lambda Functions
9
Section 3: Development Patterns
12
Section 4: Architectures and Use Cases

Building a batch data processing workload

It's hard to know just where to start when you approach building your first data processing workload. The data you are working with is not the same as the data other people are working with, so the examples are sometimes challenging to apply.

In this section, we'll go through a real example to learn how to use AWS Glue to process some data. After finishing this section, you will have a grasp of the high-level methodology and be able to apply the same steps to the dataset that you are looking to process.

Where do I start?

Start with batch processing. Always start with batch! To build our first batch workload, we will use a combination of the services mentioned here:

  • S3: To...

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