Book Image

Data Wrangling on AWS

By : Navnit Shukla, Sankar M, Sampat Palani
5 (1)
Book Image

Data Wrangling on AWS

5 (1)
By: Navnit Shukla, Sankar M, Sampat Palani

Overview of this book

Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.
Table of Contents (19 chapters)
Part 1:Unleashing Data Wrangling with AWS
Part 2:Data Wrangling with AWS Tools
Part 3:AWS Data Management and Analysis
Part 4:Advanced Data Manipulation and ML Data Optimization
Part 5:Ensuring Data Lake Security and Monitoring

SageMaker Studio setup prerequisites

SageMaker Data Wrangler is available as a service within Amazon SageMaker Studio. While you can still use some of the SageMaker Data Wrangler features via APIs, for the purposes of this book, we will be using Data Wrangler from within SageMaker Studio. In this section, we will cover a brief overview of SageMaker Studio and how to set up a SageMaker Studio domain and users in your AWS account.


Before we can start setting up SageMaker Studio, there are a few prerequisites, as follows:

  • An AWS account.
  • An Identity and Access Management (IAM) role with the appropriate policy and permissions attached. There is an AmazonSageMakerFullAccess AWS managed policy that you can use as is or as a starting point to create your custom policy.

Studio domain

You will start by creating and onboarding a SageMaker domain using the AWS console. A SageMaker domain includes an Amazon Elastic File System (Amazon EFS) volume, a list...