The course is part of this learning path
Learning Objectives
This course is an introductory level AWS development course. 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 course 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 course, you must meet the AWS Developer Associate certification requirements or have equivalent experience.
This course 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 courses here at Cloud Academy:
3) Introduction to the AWS CLI
4) How to Use the AWS Command-Line Interface
Summary. In this course, we discussed the AWS Data Wrangler as an open source initiative developed by the AWS Professional Services Team. Data Wrangler is a Python library built using Pandas, Bow 23 and PI Arrow, to allow you to perform data extraction, data transformations, and load operations on data pipelines using AWS Data Services. For Cloud Academy, this is Jorge Negron. Thanks for watching.
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).