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

Introducing AWS SDK for pandas

In this chapter, we will explore the AWS SDK for pandas package and understand its building blocks. We will explore in detail different modes of installing the AWS SDK for pandas package for various use cases and will also show you how to customize the package further to suit your specific project needs. We will also learn the integrations of this package with AWS services and how it helps to perform common data-wrangling activities in an efficient manner. So, by the end of this chapter, you can expect to understand AWS SDK for pandas and its building blocks, the standard and custom installation options for different use cases, and integration with AWS services such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon Athena.

This chapter covers the following topics:

  • AWS SDK for pandas
  • Building blocks for AWS SDK for pandas
  • Customizing, building, and installing AWS SDK for pandas
  • Configuration options for AWS SDK for pandas
  • ...