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

Data visualization

Data visualization is the phase where you will create visuals and charts to better communicate the findings of your analysis to business users. A picture is worth a thousand words and an idea/message can be better communicated with charts/dashboards than tables/text data.

Visualization with Python libraries

In this section, we will explore creating dashboards using Python libraries such as matplotlib ( and Seaborn ( There are more Python libraries that can help in visualizing data, but we will not compare all those libraries here.

We will use the same sports dataset for this section as well. There are other datasets such as NY taxi trips datasets that we can use to cover different visualization aspects, but we will use the sports data for continuity purposes in this chapter. Let us consider a use case, where we want to visualize the following requirements:

  • The number of tickets sold on...