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Data Wrangling on AWS

You're reading from Data Wrangling on AWS Clean and organize complex data for analysis

Product type Paperback

Published in Jul 2023

Publisher Packt

ISBN-13 9781801810906

Length 420 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Authors (3):

Sankar M

Navnit Shukla

Sam 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 FREE CHAPTER

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

A solution walkthrough for sportstickets.com

We will walk through a fictional example, sportstickets.com, which is a sports-ticketing franchise. This company manages different sporting events and sells tickets for sports events at a discounted rate. The business analysts from sportsticket.com want to set up an end-to-end data-wrangling pipeline for performing ticket sales analysis on the data.

We will explore the different phases of the data-wrangling pipeline and explain how the Pandas library will help in performing those operations in an effective and performant manner.

Figure 9.1: Different phases of the data-wrangling pipeline

Figure 9.1: Different phases of the data-wrangling pipeline

Prerequisites for data ingestion

In order to perform data-wrangling activities for the preceding use case, we need to first ingest data into a data lake. In order to ingest data from on-premise databases into a cloud environment, we have the following options:

Extract data programmatically using SQL queries from...

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Authors (3)

Shukla

Shukla

Vishal Shukla is the CEO of Brevitaz Systems (http://brevitaz.com) and a technology evangelist at heart. He is a passionate software scientist and a big data expert. Vishal has extensive experience in designing modular enterprise systems. Since his college days (more than 11 years), Vishal has enjoyed coding in JVM-based languages. He also embraces design thinking and sustainable software development. He has vast experience in architecting enterprise systems in various domains. Vishal is deeply interested in technologies related to big data engineering, analytics, and machine learning. He set up Brevitaz Systems. This company delivers massively scalable and sustainable big data and analytics-based enterprise applications to their global clientele. With varied expertise in big data technologies and architectural acumen, the Brevitaz team successfully developed and re-engineered a number of legacy systems to state-of-the-art scalable systems. Brevitaz has imbibed in its culture agile practices, such as scrum, test-driven development, continuous integration, and continuous delivery, to deliver high-quality products to its clients. Vishal is a music and art lover. He loves to sing, play musical instruments, draw portraits, and play sports, such as cricket, table tennis, and pool, in his free time. You can contact Vishal at vishal.shukla@brevitaz.com and on LinkedIn at https://in.linkedin.com/in/vishalshu. You can also follow Vishal on Twitter at @vishal1shukla2.

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Sam Palani

Sam 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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Sankar M

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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