Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions
- Length: 384 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2021-07-23
- ISBN-10: 1800569688
- ISBN-13: 9781800569683
- Sales Rank: #427115 (See Top 100 Books)
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. You’ll 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 Redshift’s columnar architecture and managed services. As you advance, you’ll 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, you’ll 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, you’ll 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 Redshift’s 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
- Getting Started with Amazon Redshift
- Data Management
- Loading & Unloading data
- Data Pipelines
- Scalable Data Orchestration for Automation
- Data Authorization & Security
- Performance Optimization
- Cost Optimization
- Lake House Architecture
- Extending Redshift Capabilities
Amazon Redshift Cookbook Foreword Contributors About the authors About the reviewers Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Share Your Thoughts Chapter 1: Getting Started with Amazon Redshift Technical requirements Creating an Amazon Redshift cluster using the AWS Console Getting ready How to do it… Creating an Amazon Redshift cluster using the AWS CLI Getting ready How to do it… How it works… Creating an Amazon Redshift cluster using an AWS CloudFormation template Getting ready How to do it… How it works… Connecting to an Amazon Redshift cluster using the Query Editor Getting ready How to do it… Connecting to an Amazon Redshift cluster using the SQL Workbench/J client Getting ready How to do it… Connecting to an Amazon Redshift Cluster using a Jupyter Notebook Getting ready How to do it… Connecting to an Amazon Redshift cluster using Python Getting ready How to do it… Connecting to an Amazon Redshift cluster programmatically using Java Getting ready How to do it… Connecting to an Amazon Redshift cluster programmatically using .NET Getting ready How to do it… Connecting to an Amazon Redshift cluster using the command line Getting ready How to do it… Chapter 2: Data Management Technical requirements Managing a database in an Amazon Redshift cluster Getting ready How to do it… Managing a schema in a database Getting ready How to do it… Managing tables Getting ready How to do it… How it works… Managing views Getting ready How to do it… Managing materialized views Getting ready How to do it… How it works… Managing stored procedures Getting ready How to do it… How it works… Managing UDFs Getting ready How to do it… How it works… Chapter 3: Loading and Unloading Data Technical requirements Loading data from Amazon S3 using COPY Getting ready How to do it… How it works… Loading data from Amazon EMR Getting ready How to do it… Loading data from Amazon DynamoDB Getting ready How to do it… How it works… Loading data from remote hosts Getting ready How to do it… Updating and inserting data Getting ready How to do it… Unloading data to Amazon S3 Getting ready How to do it… Chapter 4: Data Pipelines Technical requirements Ingesting data from transactional sources using AWS DMS Getting ready How to do it… How it works… Streaming data to Amazon Redshift via Amazon Kinesis Firehose Getting ready How to do it… How it works… Cataloging and ingesting data using AWS Glue How to do it… How it works… Chapter 5: Scalable Data Orchestration for Automation Technical requirements Scheduling queries using the Amazon Redshift query editor Getting ready How to do it… How it works… Event-driven applications using Amazon EventBridge and the Amazon Redshift Data API Getting ready How to do it… How it works… Event-driven applications using AWS Lambda Getting ready How to do it… How it works… Orchestrating using AWS Step Functions Getting ready How to do it… How it works… Orchestrating using Amazon MWAA Getting ready How to do it… How it works… Chapter 6: Data Authorization and Security Technical requirements Managing infrastructure security Getting ready How to do it Data encryption at rest Getting ready How to do it Data encryption in transit Getting ready How to do it Column-level security Getting ready How to do it How it works Loading and unloading encrypted data Getting ready How to do it Managing superusers Getting ready How to do it Managing users and groups Getting ready How to do it Managing federated authentication Getting ready How to do it How it works Using IAM authentication to generate database user credentials Getting ready How to do it Managing audit logs Getting ready How to do it How it works Monitoring Amazon Redshift Getting ready How to do it How it works Chapter 7: Performance Optimization Technical requirements Amazon Redshift Advisor Getting ready How to do it… How it works… Managing column compression Getting ready How to do it… How it works… Managing data distribution Getting ready How to do it… How it works… Managing sort keys Getting ready How to do it… How it works… Analyzing and improving queries Getting ready How to do it… How it works… Configuring workload management (WLM) Getting ready How to do it… How it works… Utilizing Concurrency Scaling Getting ready How to do it… How it works… Optimizing Spectrum queries Getting ready How to do it… How it works… Chapter 8: Cost Optimization Technical requirements AWS Trusted Advisor Getting ready How to do it… How it works… Amazon Redshift Reserved Instance pricing Getting ready How to do it… Configuring pause and resume for an Amazon Redshift cluster Getting ready How to do it… Scheduling pause and resume Getting ready How to do it… How it works… Configuring Elastic Resize for an Amazon Redshift cluster Getting ready How to do it… Scheduling Elastic Resizing Getting ready How to do it… How it works… Using cost controls to set actions for Redshift Spectrum Getting ready How to do it… Using cost controls to set actions for Concurrency Scaling Getting ready How to do it… Chapter 9: Lake House Architecture Technical requirements Building a data lake catalog using AWS Lake Formation Getting ready How to do it… How it works… Exporting a data lake from Amazon Redshift Getting ready How to do it… Extending a data warehouse using Amazon Redshift Spectrum Getting ready How to do it… Data sharing across multiple Amazon Redshift clusters Getting ready How to do it… How it works… Querying operational sources using Federated Query Getting ready How to do it… Chapter 10: Extending Redshift's Capabilities Technical requirements Managing Amazon Redshift ML Getting ready How to do it… How it works… Visualizing data using Amazon QuickSight Getting ready How to do it… How it works… AppFlow for ingesting SaaS data in Redshift Getting ready How to do it… How it works… Data wrangling using DataBrew Getting ready How to do it… How it works… Utilizing ElastiCache for sub-second latency Getting ready How to do it… How it works… Subscribing to third-party data using AWS Data Exchange Getting ready How to do it… How it works… Appendix Recipe 1 – Creating an IAM user Recipe 2 – Storing database credentials using Amazon Secrets Manager Recipe 3 – Creating an IAM role for an AWS service Recipe 4 – Attaching an IAM role to the Amazon Redshift cluster Why subscribe? 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