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

Getting Started with Data Wrangling

In the introductory section of this book, we listed use cases regarding how organizations use data to bring value to customers. Apart from that, organizations collect a lot of other data so that they can understand the finances of customers, which helps them share it with stakeholders, including log data for security, system health checks, and customer data, which is required for working on use cases such as Customer 360s.

We talked about all these use cases and how collecting data from different data sources is required to solve them. However, from collecting data to solving these business use cases, one very important step is to clean the data. That is where data wrangling comes into the picture.

In this chapter, we are going to learn the basics of data wrangling and cover the following topics:

  • Introducing data wrangling
  • The steps involved in data wrangling
  • Best practices for data wrangling
  • Options available within Amazon Web Services (AWS) to perform data wrangling