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


In this chapter, we have discussed what big data is, the characteristics of big data, what a data lake is, why we need data lakes, and how a data lake can be built on Amazon S3 by providing an overview of the benefits of data lakes, the different layers of a data lake, and the best practices for building a data lake on Amazon S3. We also provided details on organizing and managing the data within a data lake on S3, including using features such as file formats, partitions, S3 lifecycle management, Amazon S3 Intelligent-Tiering, and so on. The chapter also discussed some challenges and considerations when building a data lake on Amazon S3, such as cost and performance.

In the next chapter, we are going to learn about AWS Glue. AWS Glue is a data integration service that lets you bring data from different data sources and allows you to perform ETL on top of it using frameworks such as Apache Spark and Python.