Using AWS Data Wrangler for ETL Pipelines With AWS Data Services

Beginner
3m
183
5/5

Learning Objectives

This lesson is an introductory level AWS development lesson. You will learn about the AWS Data Wrangler library, what it does, and how to set it up to be able to use it. 

Intended Audience

This lesson is intended for AWS Python developers familiar with the Pandas and PyArrow libraries who are building non-distributed pipelines using AWS services. The AWS Data Wrangler library provides an abstraction for connectivity, extract, and load operations on AWS services. 

Prerequisites

To get the most out of this lesson, you must meet the AWS Developer Associate certification requirements or have equivalent experience.

This lesson expects that you are familiar with and have an existing Python development environment and have set up the AWS CLI or SDK with the required configuration and keys. Familiarity with Python syntax is also a requirement. We walk through the basic setup for some of these but do not provide detailed explanations of the process. 

For fundamentals and additional details about these skills, you can refer to the following lessons here at Cloud Academy:  

1) Python for Beginners 

2) Data Wrangling With Pandas

3) Introduction to the AWS CLI 

4) How to Use the AWS Command-Line Interface

 

About the Author
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Jorge Negrón
AWS Content Architect
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Experienced in architecture and delivery of cloud-based solutions, the development, and delivery of technical training, defining requirements, use cases, and validating architectures for results. Excellent leadership, communication, and presentation skills with attention to details. Hands-on administration/development experience with the ability to mentor and train current & emerging technologies, (Cloud, ML, IoT, Microservices, Big Data & Analytics).

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