Amazon Redshift The Definitive Guide Jump-Start Analytics Using Cloud Data Warehousing (Rajesh Francis, Rajiv Gupta, Milind Oke) (Z-Library)

Rajesh Francis, Rajiv Gupta & Milind Oke Amazon Redshift The Definitive Guide Jump-Start Analytics Using Cloud Data Warehousing

DATA “Amazon Redshift has significantly evolved in the past few years to meet growing customer demands. Rajiv Gupta, Milind Oke, and Rajesh Francis have succinctly organized these exciting features into a book that helps you get started and explore the different use cases.” —Neeraja Rentachintala Director, Amazon Redshift Product Management Amazon Redshift: The Definitive Guide Twitter: @oreillymedia linkedin.com/company/oreilly-media youtube.com/oreillymedia Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprises. This practical guide thoroughly examines this managed service and demonstrates how you can extract value from your data immediately using serverless or a provisioned option to scale according to your workloads, and you only pay for what you use. Analytic specialists Rajesh Francis, Rajiv Gupta, and Milind Oke detail Amazon Redshift’s architecture and rich feature set and describe how Amazon Redshift is designed to meet ever-growing data use cases. Whether you’re a data engineer, data scientist, data analyst, or a database administrator, this book helps you get the most value from Amazon Redshift. You’ll learn how to: • Build a cloud data strategy around Amazon Redshift • Get started with Redshift’s simple-to-use data models • Determine how and when to use Redshift serverless and provisioned data warehouses • Take advantage of Redshift’s auto-tuning options • Load and transform massive amounts of data in batch and real-time • Enable users to perform predictive analytics using Amazon Redshift ML • Use data sharing to break apart data monoliths and connect data silos • Learn best practices for security, monitoring, resilience, and disaster recovery Rajesh Francis, analytics specialist at AWS, is responsible for driving data strategy and Amazon Redshift adoption. Rajiv Gupta, manager of analytics specialist solutions architects, has worked in the data warehousing space for 20 years. Milind Oke is an analytics specialist with decades of experience in architecting enterprise-grade data warehousing solutions and platforms. US $79.99 CAN $99.99 ISBN: 978-1-098-13530-0

Rajesh Francis, Rajiv Gupta, and Milind Oke Amazon Redshift: The Definitive Guide Jump-Start Analytics Using Cloud Data Warehousing Boston Farnham Sebastopol TokyoBeijing

978-1-098-13530-0 [LSI] Amazon Redshift: The Definitive Guide by Rajesh Francis, Rajiv Gupta, and Milind Oke Copyright © 2024 Rajesh Francis, Rajiv Gupta, and Milind Oke. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Aaron Black Development Editor: Sara Hunter Production Editor: Christopher Faucher Copyeditor: Piper Editorial Consulting, LLC Proofreader: Stephanie English Indexer: Sue Klefstad Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Kate Dullea October 2023: First Edition Revision History for the First Edition 2023-10-02: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098135300 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Amazon Redshift: The Definitive Guide, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. This work is part of a collaboration between O’Reilly and AWS. See our statement of editorial independence.

Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1. AWS for Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Data-Driven Organizations 2 Business Use Cases 4 New Business Use Cases with Generative AI 6 Modern Data Strategy 7 Comprehensive Set of Capabilities 9 Integrated Set of Tools 12 End-to-End Data Governance 13 Modern Data Architecture 14 Role of Amazon Redshift in a Modern Data Architecture 15 Real-World Benefits of Adopting a Modern Data Architecture 19 Reference Architecture for Modern Data Architecture 20 Data Sourcing 21 Extract, Transform, and Load 22 Storage 22 Analysis 24 Data Mesh and Data Fabric 26 Data Mesh 26 Data Fabric 28 Summary 29 2. Getting Started with Amazon Redshift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Amazon Redshift Architecture Overview 32 Get Started with Amazon Redshift Serverless 35 iii

Creating an Amazon Redshift Serverless Data Warehouse 35 Sample Data 40 Activate Sample Data Models and Query Using the Query Editor 40 When to Use a Provisioned Cluster? 44 Creating an Amazon Redshift Provisioned Cluster 46 Estimate Your Amazon Redshift Cost 51 Amazon Redshift Managed Storage 51 Amazon Redshift Serverless Compute Cost 51 Amazon Redshift Provisioned Compute Cost 54 AWS Account Management 56 Connecting to Your Amazon Redshift Data Warehouse 58 Private/Public VPC and Secure Access 59 Stored Password 60 Temporary Credentials 61 Federated User 62 SAML-Based Authentication from an Identity Provider 63 Native IdP Integration 65 Amazon Redshift Data API 66 Querying a Database Using the Query Editor V2 67 Business Intelligence Using Amazon QuickSight 74 Connecting to Amazon Redshift Using JDBC/ODBC 78 Summary 80 3. Setting Up Your Data Models and Ingesting Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Data Lake First Versus Data Warehouse First Strategy 81 Data Lake First Strategy 82 Data Warehouse First Strategy 82 Deciding On a Strategy 83 Defining Your Data Model 84 Database Schemas, Users, and Groups 85 Star Schema, Denormalized, Normalized 85 Student Information Learning Analytics Dataset 88 Create Data Models for Student Information Learning Analytics Dataset 90 Load Batch Data into Amazon Redshift 93 Using the COPY Command 93 Ingest Data for the Student Learning Analytics Dataset 94 Building a Star Schema 95 Continuous File Ingestion from Amazon S3 99 Using AWS Glue for Transformations 100 Manual Loading Using SQL Commands 101 Using the Query Editor V2 102 Load Real-Time and Near Real-Time Data 104 iv | Table of Contents

Near Real-Time Replication Using AWS Database Migration Service 104 Amazon Aurora Zero-ETL Integration with Amazon Redshift 105 Using Amazon AppFlow 107 Streaming Ingestion 109 Optimize Your Data Structures 114 Automatic Table Optimization and Autonomics 115 Distribution Style 115 Sort Key 117 Compression Encoding 120 Summary 121 4. Data Transformation Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Comparing ELT and ETL Strategies 124 In-Database Transformation 125 Semistructured Data 125 User-Defined Functions 129 Stored Procedures 132 Scheduling and Orchestration 134 Access All Your Data 139 External Amazon S3 Data 139 External Operational Data 142 External Amazon Redshift Data 144 External Transformation 146 AWS Glue 148 Summary 157 5. Scaling and Performance Optimizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Scale Storage 161 Autoscale Your Serverless Data Warehouse 162 Scale Your Provisioned Data Warehouse 162 Evolving Compute Demand 163 Unpredictable Workload Changes 166 WLM, Queues, and QMR 169 Queue Assignment 169 Short Query Acceleration 172 Query Monitoring Rules 173 Automatic WLM 177 Manual WLM 180 Parameter Group 183 WLM Dynamic Memory Allocation 184 Materialized Views 184 Autonomics 185 Table of Contents | v

Auto Table Optimizer and Smart Defaults 186 Auto Vacuum 187 Auto Vacuum Sort 188 Auto Analyze 189 Auto Materialized Views (AutoMV) 189 Amazon Redshift Advisor 190 Workload Isolation 191 Additional Optimizations for Achieving the Best Price and Performance 192 Database Versus Data Warehouse 192 Amazon Redshift Serverless 192 Multi-Warehouse Environment 193 AWS Data Exchange 193 Table Design 194 Indexes Versus Zone Maps 194 Drivers 194 Simplify ETL 194 Query Editor V2 195 Query Tuning 195 Query Processing 195 Analyzing Queries 204 Identifying Queries for Performance Tuning 205 Summary 206 6. Amazon Redshift Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Machine Learning Cycle 208 Amazon Redshift ML 210 Amazon Redshift ML Flexibility 211 Getting Started with Amazon Redshift ML 213 Machine Learning Techniques 214 Supervised Learning Techniques 214 Unsupervised Learning Techniques 216 Machine Learning Algorithms 216 Integration with Amazon SageMaker Autopilot 218 Create Model 218 Label Probability 223 Explain Model 224 Using Amazon Redshift ML to Predict Student Outcomes 225 Amazon SageMaker Integration with Amazon Redshift 228 Integration with Amazon SageMaker—Bring Your Own Model (BYOM) 229 BYOM Local 229 BYOM Remote 230 Amazon Redshift ML Costs 230 vi | Table of Contents

Summary 232 7. Collaboration with Data Sharing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Amazon Redshift Data Sharing Overview 234 Data Sharing Use Cases 235 Key Concepts of Data Sharing 237 How to Use Data Sharing 240 Sharing Data Within the Same Account 240 Sharing Data Across Accounts Using Cross-Account Data Sharing 243 Analytics as a Service Use Case with Multi-Tenant Storage Patterns 246 Scaling Your Multi-tenant Architecture Using Data Sharing 246 Multi-tenant Storage Patterns Using Data Sharing 247 External Data Sharing with AWS ADX Integration 264 Publishing a Data Product 265 Subscribing to a Published Data Product 268 Considerations When Using AWS Data Exchange for Amazon Redshift 269 Query from the Data Lake and Unload to the Data Lake 270 Amazon DataZone to Discover and Share Data 271 Use Cases for a Data Mesh Architecture with Amazon DataZone 272 Key Capabilities and Use Cases for Amazon DataZone 273 Amazon DataZone Integrations with Amazon Redshift and Other AWS Services 274 Components and Capabilities of Amazon DataZone 274 Getting Started with Amazon DataZone 277 Security in Amazon DataZone 284 Summary 285 8. Securing and Governing Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Object-Level Access Controls 287 Object Ownership 289 Default Privileges 289 Public Schema and Search Path 290 Access Controls in Action 290 Database Roles 293 Database Roles in Action 294 Row-Level Security 295 Row-Level Security in Action 296 Row-Level Security Considerations 298 Dynamic Data Masking 298 Dynamic Data Masking in Action 299 Dynamic Data Masking Considerations 302 External Data Access Control 303 Table of Contents | vii

Associate IAM Roles 303 Authorize Assume Role Privileges 305 Establish External Schemas 306 Lake Formation for Fine-Grained Access Control 308 Summary 309 9. Migrating to Amazon Redshift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Migration Considerations 312 Retire Versus Retain 312 Migration Data Size 313 Platform-Specific Transformations Required 314 Data Volatility and Availability Requirements 314 Selection of Migration and ETL Tools 315 Data Movement Considerations 315 Domain Name System (DNS) 315 Migration Strategies 315 One-Step Migration 316 Two-Step Migration 316 Iterative Migration 317 Migration Tools and Services 318 AWS Schema Conversion Tool 319 Data Warehouse Migration Service 323 AWS Snow Family 328 AWS Snowball Edge Client 331 Database Migration Process 331 Step 1: Convert Schema and Subject Area 332 Step 2: Initial Data Extraction and Load 332 Step 3: Incremental Load Through Data Capture 333 Amazon Redshift Migration Tools Considerations 334 Accelerate Your Migration to Amazon Redshift 335 Macro Conversion 336 Case-Insensitive String Comparison 337 Recursive Common Table Expressions 339 Proprietary Data Types 339 Summary 342 10. Monitoring and Administration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Amazon Redshift Monitoring Overview 344 Monitoring 344 Troubleshooting 345 Optimization 345 Monitoring Using Console 345 viii | Table of Contents

Monitoring and Administering Serverless 346 Monitoring Provisioned Data Warehouse Using Console 353 Monitoring Queries and Loads Across Clusters 369 Identifying Systemic Query Performance Problems 373 Monitoring Using Amazon CloudWatch 374 Amazon Redshift CloudWatch Metrics 375 Monitoring Using System Tables and Views 379 Monitoring Serverless Using System Views 381 High Availability and Disaster Recovery 384 Recovery Time Objective and Recovery Point Objective Considerations 384 Multi-AZ Compared to Single-AZ Deployment 385 Creating or Converting a Provisioned Data Warehouse with Multi-AZ Configuration 387 Auto Recovery of Multi-AZ Deployment 389 Snapshots, Backup, and Restore 391 Snapshots for Backup 391 Automated Snapshots 391 Manual Snapshots 392 Disaster Recovery Using Cross-Region Snapshots 393 Using Snapshots for Simple-Replay 395 Monitoring Amazon Redshift Using CloudTrail 395 Bring Your Own Visualization Tool to Monitor Amazon Redshift 396 Monitor Operational Metrics Using System Tables and Amazon QuickSight 397 Monitor Operational Metrics Using Grafana Plug-in for Amazon Redshift 397 Summary 399 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Table of Contents | ix

Foreword In today’s data-driven world, organizations are constantly looking to extract actiona‐ ble business insights from the vast amount of data at their disposal and turn it into a competitive advantage by offering seamless experiences to their customers and streamlining business operations. The ability to efficiently store, manage, and enable access to data assets for all users in the organization in a secure and governed fashion is a critical requirement that requires rethinking the traditional data architectures. Cloud data warehousing has emerged as a central pillar of this modern data architec‐ ture in the past decade. Amazon Redshift, developed by Amazon Web Services (AWS), has been at the fore‐ front of this revolution ever since it was launched as the first cloud data warehouse in 2013, empowering businesses to scale their data warehousing and do it cost effec‐ tively unlocking the full potential of their data . Tens of thousands of organizations have adopted Amazon Redshift as the foundational data store for their modern data strategy to serve a wide spectrum of analytics requirements. As a data leader, I am thrilled to introduce this comprehensive guide to Amazon Redshift, which is written for both seasoned data professionals and those new to the world of cloud data warehousing. In Amazon Redshift: The Definitive Guide, the authors take a solutions architecture approach and offer practical insights, best practices, and real-world examples that will enable you to harness the full power of this service. The authors have extensive experience building solutions for hundreds of customers of all types, from startups to global organizations. In particular, they’ve helped projects ranging from migra‐ tions from other data warehouses to Amazon Redshift and run then with high performance at scale, to delivering new analytics use cases such as real time analytics leveraging Redshift streaming ingestion and zero-ETL, building predictive analytics solutions using Redshift ML, and even go beyond to offer analytics as a service using powerful capabilities such as Redshift data sharing. xi

The book starts by laying the foundations of modern data strategy, explaining the fundamental concepts of data warehouse and architecture of Amazon Redshift. From there, the authors dive deep into key features of the service with relevant examples for data modeling, loading and transforming data including streaming ingestion, performance optimization, scaling, and security best practices. You will also find in-depth coverage of integration with other AWS services like Amazon SageMaker, Amazon Aurora ensuring you can leverage the broader AWS ecosystem to build robust and scalable data applications. What sets this book apart is its practical approach. The authors have drawn upon their experience as solutions architects working with Amazon Redshift to provide real-world use cases that will help you build a scalable data architecture with best practices. From an executive seeking to make informed decisions about your organi‐ zation’s data strategy, to a data engineer responsible for curating and managing data, or a data analyst looking to get business insights from your data, this book has something to offer for all types of readers. As we continue to navigate an increasingly complex data landscape, building a data strategy around Amazon Redshift can help you simplify your data architecture. Beyond managing data, Amazon Redshift can be a catalyst for innovation in your organization to help you derive meaningful insights at scale and speed. I have seen the transformative impact that Amazon Redshift can have on businesses, and I’m confident that this book will empower you to achieve similar success on your data journey. I commend the authors for their initiative and effort to create this comprehensive guide to Amazon Redshift. This book has information that will help readers start‐ ing their data modernization journey on cloud or readers who are just seeking to understand data architecture patterns. I encourage you to learn and apply the principles and techniques outlined in this book to drive your organization to become data-driven, and improve customer experience. — Neeraja Rentachintala Director, Amazon Redshift Product Management xii | Foreword

Preface Welcome to the world of data warehousing and Amazon Redshift! In this book, we embark on an exciting journey that explores the powerful capabilities of Amazon Redshift and its role in modern data warehousing. Whether you are a data professio‐ nal, architect, IT leader, or simply someone curious about data management and analytics, this book is designed to provide you with comprehensive insights into modern data warehousing patterns using Amazon Redshift. Data plays a pivotal role in modern business operations, serving as a valuable asset that fuels informed decision making to drive growth. In today’s digital age, businesses generate and collect vast amounts of data from various sources, including customer interactions, market trends, social media, devices, and operational processes. By harnessing and analyzing this data, businesses can gain competitive advantage by identifying patterns and correlations to make data-driven decisions and drive innova‐ tion. As the volume, velocity, and variety of data continue to grow exponentially, it has become increasingly crucial for businesses to have efficient and scalable data warehousing solutions that can handle the demands of today’s data-driven world. Amazon Redshift, a fully managed, cloud-based data warehousing service, has emerged as a leading solution in the industry, empowering organizations to store, analyze, and gain actionable insights from their vast datasets. With its flexible architecture, high-performance processing capabilities, and integration with other Amazon Web Services (AWS), Amazon Redshift provides a platform for building robust and scalable data warehouses. Amazon Redshift has been at the forefront in the Gartner Database Management System (DBMS) Magic Quadrant, and this book will provide extra insight on how to successfully implement your analytical solutions on this data warehousing service from AWS. Amazon Redshift has evolved from a standalone analytical query engine to an AI-powered data warehouse service leveraging machine learning (ML) at the core of its features like automatic workload management, Autonomics, and Code‐ Whisperer in Query Editor. xiii

In this book, we delve into the fundamental concepts and principles of data ware‐ housing, covering topics such as data modeling; extract, transform, and load pro‐ cesses; performance optimization; and data governance. We explore the unique features and advantages of Amazon Redshift, guiding you through the process of setting up, configuring, and managing your Redshift clusters. We will also discuss best practices for data loading, schema design, query optimization, and security considerations. This book is equally apt for personnel completely new to data warehousing or those who are looking to modernize their current on-premise solutions by leveraging the power of the cloud. The chapters have been organized to first introduce the Amazon Redshift service and the focus shifts toward migration in Chapter 9. But we encour‐ age readers interested in migration to Amazon Redshift to review Chapter 9 earlier as they see fit. We have used our personal experience with Amazon Redshift, along with our interac‐ tions with customers using Amazon Redshift, which is a privilege we earn from our day jobs. Also being close to the actual product teams and engineering teams building out this service has assisted us in sharing some interesting pieces throughout the book. We took almost an entire calendar year to put this book together. AWS is ever evolving its services based on customer feedback, every few months rolling out new features, and we are looking forward to seeing how soon this book gets “outdated,” or should we say, we are rooting for it! As you progress through each chapter, you will gain a deeper understanding of how to leverage the power of Amazon Redshift to build a modern data warehouse that can handle large volumes of data, support complex analytical queries, and facilitate real-time insights. We provide practical examples, code snippets, and real-world sce‐ narios to help you apply the concepts and techniques to your own data warehousing projects. It is important to note that this book assumes no prior knowledge of Amazon Redshift or data warehousing concepts. We start with the basics and gradually build upon them, ensuring that readers of all levels can benefit from this comprehensive guide. Whether you are just beginning your data warehousing journey or seeking to enhance your existing knowledge, this book will serve as a valuable resource and reference. Without further ado, let’s embark on this exciting journey into the world of data warehousing with Amazon Redshift. May this book serve as a trusted compan‐ ion, equipping you with the knowledge and tools necessary to build scalable, high- performance data warehouses and transform your organization’s data into a strategic asset. xiv | Preface

Happy reading, and may your data endeavors be successful! Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a tip or suggestion. This element signifies a general note. This element indicates a warning or caution. Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://resources.oreilly.com/examples/0636920746867. If you have a technical question or a problem using the code examples, please send email to bookquestions@oreilly.com. Preface | xv

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Amazon Redshift: The Definitive Guide by Rajesh Francis, Rajiv Gupta, and Milind Oke (O’Reilly). Copy‐ right 2024 Rajesh Francis, Rajiv Gupta, and Milind Oke, 978-1-098-13530-0.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. O’Reilly Online Learning For more than 40 years, O’Reilly Media has provided technol‐ ogy and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-889-8969 (in the United States or Canada) 707-829-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://www.oreilly.com/about/contact.html xvi | Preface

We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at https://oreil.ly/amazon-redshift-definitive- guide. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly-media. Follow us on Twitter: https://twitter.com/oreillymedia. Watch us on YouTube: https://youtube.com/oreillymedia. Acknowledgments We would like to acknowledge the entire Amazon Redshift customer base, the various AWS teams working on developing this service, the product managers, the engineers, the solutions architects field teams, and also the product marketing teams. Without support from all these teams, this endeavor would simply not have been possible! Preface | xvii