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

Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas

In the previous chapters, we learned about the data-wrangling process and how to utilize different services for data-wrangling activities within the AWS ecosystem:

  • We explored AWS Glue DataBrew, which helps you in creating a data-wrangling pipeline through a GUI-based approach for every type of user.
  • We also went through SageMaker Data Wrangler, which also helps users in creating a GUI-based data-wrangling pipeline, but it’s more closely aligned with machine learning workloads with tighter integration with the SageMaker service.
  • We also explored AWS SDK for Pandas, aka awswrangler, which is a hands-on coding approach to data wrangling that integrates the Pandas library with the AWS ecosystem. This will be used by users who are more hands-on with Python programming and are in love with the Pandas library and its capabilities.
  • We also went through different AWS services such as Amazon S3...