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

A solution walkthrough for

We will walk through a fictional example,, which is a sports-ticketing franchise. This company manages different sporting events and sells tickets for sports events at a discounted rate. The business analysts from want to set up an end-to-end data-wrangling pipeline for performing ticket sales analysis on the data.

We will explore the different phases of the data-wrangling pipeline and explain how the Pandas library will help in performing those operations in an effective and performant manner.

Figure 9.1: Different phases of the data-wrangling pipeline

Figure 9.1: Different phases of the data-wrangling pipeline

Prerequisites for data ingestion

In order to perform data-wrangling activities for the preceding use case, we need to first ingest data into a data lake. In order to ingest data from on-premise databases into a cloud environment, we have the following options:

  1. Extract data programmatically using SQL queries from...