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

Data Processing for Machine Learning with SageMaker Data Wrangler

In Chapter 4, we introduced you to SageMaker Data Wrangler, a purpose-built tool to process data for machine learning. We discussed why data processing for machine learning is such a critical component of the overall machine learning pipeline and some risks of working with unclean or raw data. We also covered the core capabilities of SageMaker Data Wrangler and how it is helpful to solve some key challenges involved in data processing for machine learning.

In this chapter, we will take things further by taking a practical step-by-step data flow to preprocess an example dataset for machine learning. We will start by taking an example dataset that comes preloaded with SageMaker Data Wrangler and then do some basic exploratory data analysis using Data Wrangler built-in analysis. We will also add a couple of custom checks for imbalance and bias in the dataset. Feature engineering is a key step in the machine learning...