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 covered an introduction to data processing on AWS, specifically focusing on ML and data science. We looked at how data processing for ML is unique and why it is such a critical and significant component of the overall ML workflow. We went through some of the challenges when dealing with large and distributed datasets and data sources and how to work with these at scale. We discussed the importance of having a reliable and repeatable data processing workflow for ML. We then covered some of the key capabilities that are needed in tooling and the frameworks used for data processing for ML, which include the ability to detect bias present in real-world data, the ability to detect and fix data imbalances, the ability to perform quick and error-free transformations and run preprocessing reports and visualizations at scale, as well as the ability to ingest data at scale.

As enterprises move from experimentation and research to production, the focus switches...