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Amazon Redshift Cookbook

You're reading from Amazon Redshift Cookbook Recipes for building modern data warehousing solutions

Product type Paperback

Published in Jul 2021

Publisher Packt

ISBN-13 9781800569683

Length 384 pages

Edition 1st Edition

Languages

Python

Tools

Amazon Redshift

Concepts

Data Analysis

Authors (3):

Shruti Worlikar

Harshida Patel

Thiyagarajan Arumugam

View More author details

Table of Contents (13) Chapters

Preface

1. Chapter 1: Getting Started with Amazon Redshift

2. Chapter 2: Data Management FREE CHAPTER

3. Chapter 3: Loading and Unloading Data

4. Chapter 4: Data Pipelines

5. Chapter 5: Scalable Data Orchestration for Automation

6. Chapter 6: Data Authorization and Security

7. Chapter 7: Performance Optimization

8. Chapter 8: Cost Optimization

9. Chapter 9: Lake House Architecture

10. Chapter 10: Extending Redshift's Capabilities

11. Other Books You May Enjoy

Appendix

Managing column compression

Amazon Redshift's columnar architecture stores data columns upon columns on disk. Analytical queries select a subset of the columns and perform aggregation on millions to billions of records. The columnar architecture reduces the I/O by selecting a subset of the columns, thus improving query performance. When data is ingested into the Amazon Redshift table, it provides three to four times compression. This further reduces the storage footprint, which, in turn, reduces I/O and hence improves query performance. Reducing the storage footprint also saves you money. Amazon Redshift Advisor provides recommendations for compressing any uncompressed tables.

In this recipe, you will learn how Amazon Redshift automatically applies compression to new and existing tables. You will also learn how column-level compression can be modified for existing columns.

Getting ready

To complete this recipe, you will need the following:

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Authors (3)

Shruti Worlikar

Shruti Worlikar

Shruti Worlikar is a cloud professional with technical expertise in data lakes and analytics across cloud platforms. Her background has led her to become an expert in on-premises-to-cloud migrations and building cloud-based scalable analytics applications. Shruti earned her bachelor's degree in electronics and telecommunications from Mumbai University in 2009 and later earned her masters' degree in telecommunications and network management from Syracuse University in 2011. Her work history includes work at J.P. Morgan Chase, MicroStrategy, and Amazon Web Services (AWS). She is currently working in the role of Manager, Analytics Specialist SA at AWS, helping customers to solve real-world analytics business challenges with cloud solutions and working with service teams to deliver real value. Shruti is the DC Chapter Director for the non-profit Women in Big Data (WiBD) and engages with chapter members to build technical and business skills to support their career advancements. Originally from Mumbai, India, Shruti currently resides in Aldie, VA, with her husband and two kids.

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Arumugam

Arumugam

Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.

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Patel

Patel

Yogesh Patel has been programming since the year 2000, and has been working in the IT industry for over ten years on a variety of technologies. He was mainly involved in Sitecore or .NET-based development in the Indian offices of US and UK-based organizations. He has worked at Investis Ltd as a technical architect and has lead a Sitecore development team. Yogesh has held a number of positions and has a lot of responsibilities, having worked at Investis since 2009. He has been recognized as a Most Valuable Professional (MVP) by Sitecore for 2014, 2015, and 2016. As a developer, Yogesh enjoys learning new programming techniques, practices, and tactics. He has worked on .NET since 2005 and Sitecore since 2009. He has also spent plenty of time developing in C, VC++, MATLAB, VB6, ASP, Java, PHP, AppleScript, SQL, and MySQL, but his current focus is on .NETespecially Sitecore. His blog can be found at http://sitecoreblog.patelyogesh.in. You can find Yogesh on Twitter at @patelyogesh_in, as well as LinkedIn (yspatel) and Facebook (patelyogesh.in).

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