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

Using AWS Glue DataBrew for data wrangling

In this section, we will explore how AWS Glue DataBrew can be utilized for various data-wrangling tasks, including the following:

  • Data discovery
  • Data structuring
  • Data cleaning
  • Data enrichment
  • Data validation
  • Data publication

Before we begin, the first step is to identify the data source, acquire the dataset, and make it available for data wrangling.

Identifying the dataset

To demonstrate the data-wrangling steps and tasks, we will be using human resources data generated using the Random HR Data Generator Excel macro available on the internet. We will be using a sample size of 5 million records with the following attributes:

Emp ID, Name Prefix, First Name, Middle Initial, Last Name, Gender, E Mail, Father's Name, Mother's Name, Mother's Maiden Name, Date of Birth, Time of Birth, Age in Yrs., Weight in Kgs., Date of Joining, Quarter of Joining, Half of Joining, Year of Joining, Month...