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Shruti Worlikar - Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions

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Shruti Worlikar Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions

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Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutions

Key Features
  • Discover how to translate familiar data warehousing concepts into Redshift implementation
  • Use impressive Redshift features to optimize development, productionizing, and operations processes
  • Find out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queries
Book Description

Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift.

This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. Youll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshifts columnar architecture and managed services. As you advance, youll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, youll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform.

By the end of this Redshift book, youll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems.

What you will learn
  • Use Amazon Redshift to build petabyte-scale data warehouses that are agile at scale
  • Integrate your data warehousing solution with a data lake using purpose-built features and services on AWS
  • Build end-to-end analytical solutions from data sourcing to consumption with the help of useful recipes
  • Leverage Redshifts comprehensive security capabilities to meet the most demanding business requirements
  • Focus on architectural insights and rationale when using analytical recipes
  • Discover best practices for working with big data to operate a fully managed solution
Who this book is for

This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, and cloud concepts and familiarity with Redshift will be beneficial.

Table of Contents
  1. Getting Started with Amazon Redshift
  2. Data Management
  3. Loading & Unloading data
  4. Data Pipelines
  5. Scalable Data Orchestration for Automation
  6. Data Authorization & Security
  7. Performance Optimization
  8. Cost Optimization
  9. Lake House Architecture
  10. Extending Redshift Capabilities

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Amazon Redshift Cookbook

Recipes for building modern data warehousing solutions

Shruti Worlikar

Thiyagarajan Arumugam

Harshida Patel

BIRMINGHAMMUMBAI Amazon Redshift Cookbook Copyright 2021 Packt Publishing All - photo 2

BIRMINGHAMMUMBAI

Amazon Redshift Cookbook

Copyright 2021 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Group Product Manager: Kunal Parikh

Publishing Product Manager: Sunith Shetty

Senior Editor: Mohammed Yusuf Imaratwale

Content Development Editor: Nazia Shaikh

Technical Editor: Arjun Varma

Copy Editor: Safis Editing

Project Coordinator: Aparna Ravikumar Nair

Proofreader: Safis Editing

Indexer: Vinayak Purushotham

Production Designer: Vijay Kamble

First published: July 2021

Production reference: 1240621

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-80056-968-3

www.packt.com

Foreword

Amazon Redshift is a fully managed cloud data warehouse house service that enables you to analyze all your data. Tens of thousands of customers use Amazon Redshift today to analyze exabytes of structured and semi-structured data across their data warehouse, operational databases, and data lake using standard SQL.

Our Analytics Specialist Solutions Architecture team at AWS work closely with customers to help use Amazon Redshift to meet their unique analytics needs. In particular, the authors of this book, Shruti, Thiyagu, and Harshida have worked hands-on with hundreds of customers of all types, from startups to multinational enterprises. Theyve helped projects ranging from migrations from other data warehouses to Amazon Redshift, to delivering new analytics use cases such as building a predictive analytics solution using Redshift ML. Theyve also helped our Amazon Redshift service team to better understand customer needs and prioritize new feature development.

I am super excited that Shruti, Thiyagu, and Harshida have authored this book, based on their deep expertise and knowledge of Amazon Redshift, to help customers quickly perform the most common tasks. This book is designed as a cookbook to provide step-by-step instructions across these different tasks. It has clear instructions on prerequisites and steps required to meet different objectives such as creating an Amazon Redshift cluster, loading data in Amazon Redshift from Amazon S3, or querying data across OLTP sources like Amazon Aurora directly from Amazon Redshift.

I recommend this book to any new or existing Amazon Redshift customer who wants to learn not only what features Amazon Redshift provides, but also how to quickly take advantage of them.

Eugene Kawamoto

Director, Product Management

Amazon Redshift, AWS

Contributors
About the authors

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.

Thiyagarajan Arumugam (Thiyagu) is a principal big data solution architect at AWS, architecting and building solutions at scale using big data to enable data-driven decisions. Prior to AWS, Thiyagu as a data engineer built big data solutions at Amazon, operating some of the largest data warehouses and migrating to and managing them. He has worked on automated data pipelines and built data lake-based platforms to manage data at scale for the customers of his data science and business analyst teams. Thiyagu is a certified AWS Solution Architect (Professional), earned his master's degree in mechanical engineering at the Indian Institute of Technology, Delhi, and is the author of several blog posts at AWS on big data. Thiyagu enjoys everything outdoors running, cycling, ultimate frisbee and is currently learning to play the Indian classical drum the mrudangam. Thiyagu currently resides in Austin, TX, with his wife and two kids.

Harshida Patel is a senior analytics specialist solution architect at AWS, enabling customers to build scalable data lake and data warehousing applications using AWS analytical services. She has presented Amazon Redshift deep-dive sessions at re:Invent. Harshida has a bachelor's degree in electronics engineering and a master's in electrical and telecommunication engineering. She has over 15 years of experience architecting and building end-to-end data pipelines in the data management space. In the past, Harshida has worked in the insurance and telecommunication industries. She enjoys traveling and spending quality time with friends and family, and she lives in Virginia with her husband and son.

About the reviewers

Anusha Challa is a senior analytics specialist solution architect at AWS with over 10 years of experience in data warehousing both on-premises and in the cloud. She has worked on multiple large-scale data projects throughout her career at Tata Consultancy Services (TCS), EY, and AWS. She has worked with hundreds of Amazon Redshift customers and has built end-to-end scalable, reliable, and robust data pipelines.

Vaidy Krishnan leads business development for AWS, helping customers successfully adopt and be successful with AWS analytics services. Prior to AWS, Vaidy spent close to 15 years building, marketing, and launching analytics products to customers in market-leading companies such as Tableau and GE across industries ranging from healthcare to manufacturing. When not at work, Vaidy likes to travel and golf.

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