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

Technical requirements

If you wish to follow along, which I highly recommend, you will need an Amazon Web Services (AWS) account. If you do not have an existing account, you can create an AWS account under the Free Tier. The AWS Free Tier provides customers with the ability to explore and try out AWS services free of charge up to specified limits for each service. If your application use exceeds the Free Tier limits, you simply pay standard, pay-as-you-go service rates. In this chapter, we will get started by looking at how to access and get familiar with the SageMaker Data Wrangler user interface. As you follow along, you will use AWS Compute and also end up creating resources in your AWS account. This especially applies to the Training a machine learning model section of the chapter, which is both compute-intensive and creates an endpoint that you will have to delete. Please remember to clean up by deleting any unused resources. We will remind you again at the end of the chapter...