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Data Wrangling on AWS

You're reading from Data Wrangling on AWS

Product type Book

Published in Jul 2023

Publisher Packt

ISBN-13 9781801810906

Pages 420 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Navnit Shukla

Sankar M

Sampat Palani

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1:Unleashing Data Wrangling with AWS

2. Chapter 1: Getting Started with Data Wrangling

3. Part 2:Data Wrangling with AWS Tools

4. Chapter 2: Introduction to AWS Glue DataBrew

5. Chapter 3: Introducing AWS SDK for pandas

6. Chapter 4: Introduction to SageMaker Data Wrangler

7. Part 3:AWS Data Management and Analysis

8. Chapter 5: Working with Amazon S3

9. Chapter 6: Working with AWS Glue

10. Chapter 7: Working with Athena

11. Chapter 8: Working with QuickSight

12. Part 4:Advanced Data Manipulation and ML Data Optimization

13. Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas

14. Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler

15. Part 5:Ensuring Data Lake Security and Monitoring

16. Chapter 11: Data Lake Security and Monitoring

17. Index

Why subscribe?

18. Other Books You May Enjoy

Why AWS Glue DataBrew?

Having the right tools is a crucial factor in enabling organizations to become data-driven, and AWS Glue DataBrew is one such tool. It is part of the AWS Glue family, which was introduced at re:Invent 2020.

Initially, when AWS Glue was launched in August 2017, it was targeted at developers and data engineers who were writing Apache Spark code. The goal was to provide them with a platform that offered both compute and storage resources to run their Spark code. This allowed them to take advantage of the speed and ease of use of Apache Spark, which is 100 times faster than Hadoop for large-scale data processing, while also leveraging the benefits of the cloud, such as elasticity, performance, and cost-effectiveness.

As the adoption of the public cloud increased and became more mainstream over time, AWS Glue evolved to meet the changing needs of enterprises. Initially, it was primarily used as an ETL tool, but it has since expanded to become a more comprehensive...

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Authors (3)

Navnit Shukla

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.

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Sankar M

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.

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Sampat Palani

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.

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