Contents
Data Wrangler
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
Using AWS Data Wrangler for ETL Pipelines with AWS Data Services. Hello, and welcome to this Cloud Academy presentation. A little bit about myself. I'm Jorge Negron and I'm part of the AWS content development team here at Cloud Academy. In this course, we'll discuss the AWS Data Wrangler Python library. This is an open source library built using BoW 2, 3 Pandas, and Apache Arrow. It was created by the AWS professional services team and continues to gain adoption among Python developers looking to build data pipelines.
It provides your Python scripts with connection, data extraction, and data load operations to some AWS services like Amazon S3, Secrets Manager, and over a dozen other data related AWS services. This course introduces the basic setup for Data Wrangler in a Python environment. It expects that the environment has been configured for AWS connectivity via the AWS CLI. We demonstrate some of these basic steps, however, we don't really explain the details and we expect you to have been able to follow these steps before.
We also demonstrate the installation and connectivity for AWS Data Wrangler and provide you some examples of interacting with Amazon S3, as well as, Secrets Manager. This course is intended for AWS Python developers, data scientists 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 data load operations on AWS services. 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.
To get the most out of this course, you will need to meet the requirements for the AWS Developer Associate certification or the equivalent experience. For the fundamental and additional details about these skills, you can refer to the following courses here at Cloud Academy. The first is Python for Beginners, the second Data wrangling with Pandas. The third, Introduction to AWS CLI, and last but not least, how to use the AWS Command Line Interface, installation and configuration. With regards to feedback, here at Cloud Academy we strive to keep our content current to provide the best training available. If you have any feedback, positive or negative, or if you notice anything that needs to be updated or corrected for the next release cycle, please reach out to us writing to support@cloudacademy.com. Thank you.
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).