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This is the code repository for Data Wrangling on AWS, published by Packt.
Clean and organize complex data for analysis
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.
This book covers the following exciting features:
- Explore how to write simple to complex transformations using AWS data wrangler
- Use abstracted functions to extract and load data from and into AWS datastores
- Configure AWS Glue DataBrew for data wrangling
- Develop data pipelines using AWS data wrangler
- Integrate AWS security features into Data Wrangler using identity and access management (IAM)
- Optimize your data with AWS SageMaker
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import sys
import awswrangler as wr
print(wr.__version__)
Following is what you need for this book: This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | AWS | Windows, Mac OS X, and Linux (Any) |
Navnit Shukla is an accomplished Senior Solution Architect with a specialization in AWS analytics.With an impressive career spanning 12 years, he has honed his expertise in databases and analytics, establishing himself as a trusted professional in the field. Currently based in Orange County, CA, Navnit's primary responsibility lies in assisting customers in building scalable, cost-effective, and secure data platforms on the AWS cloud.
Sankar Sundaram has been working in IT Industry since 2007, specializing in databases, data warehouses, analytics space for many years. As a specialized Data Architect, he helps customers build and modernize data architectures and help them build secure, scalable, and performant data lake, database, and data warehouse solutions. Prior to joining AWS, he has worked with multiple customers in implementing complex data architectures.
Sam Palani has over 18+ years as developer, data engineer, data scientist, a startup cofounder and IT leader. He holds a master's in Business Administration with a dual specialization in Information Technology. His professional career spans across 5 countries across financial services, management consulting and the technology industries. He is currently Sr Leader for Machine Learning and AI at Amazon Web Services, where he is responsible for multiple lines of the business, product strategy and thought leadership. Sam is also a practicing data scientist, a writer with multiple publications, speaker at key industry conferences and an active open source contributor. Outside work, he loves hiking, photography, experimenting with food and reading.