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

Working with AWS Glue

In the preceding chapter, we discussed various data storage types, including data warehouses, data lakes, data lakehouses, and data meshes, along with their key differences.

This chapter will explore the distinct components of AWS Glue, providing insight into how they can aid in data wrangling tasks.

After completing this chapter, you will be able to comprehend and define how AWS Glue can be utilized for data wrangling. You will also be capable of explaining the fundamental concepts associated with various AWS Glue features, such as AWS Glue Data Catalog, AWS Glue connections, AWS Glue crawlers, AWS Glue Schema Registry, AWS Glue jobs, AWS Glue development endpoints, AWS Glue interactive sessions, and AWS Glue triggers.

The following topics will be covered in this chapter:

  • Spark basics
  • AWS Glue features
  • Data discovery using AWS Glue
  • Data ingestion using AWS Glue