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  • Book Overview & Buying Amazon Redshift Cookbook
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

By : Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel
4.8 (9)
Buy this Book
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

4.8 (9)
By: Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel
Buy this Book

Overview of this book

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

Getting Started with Amazon Redshift

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth of the cost of most traditional data warehousing solutions. Amazon Redshift integrates into the data lake solution though the lakehouse architecture, allowing you to access all the structured and semi-structured data in one place. Each Amazon Redshift data warehouse is hosted as either a provisioned cluster or serverless. The Amazon Redshift provisioned data warehouse consists of one leader node and a collection of one or more compute nodes, which you can scale up or down as needed. The Amazon Redshift serverless data warehouse’s resources are automatically provisioned, and data warehouse capacity is intelligently scaled based on workload patterns. This chapter walks you through the process of creating a sample Amazon Redshift resource and connecting to it from different clients.

The following recipes are discussed in this chapter:

  • Creating an Amazon Redshift Serverless data warehouse using the AWS console
  • Creating an Amazon Redshift provisioned cluster using the AWS console
  • Creating an Amazon Redshift Serverless cluster using AWS CloudFormation
  • Creating an Amazon Redshift provisioned cluster using AWS CloudFormation
  • Connecting to a data warehouse using Amazon Redshift query editor v2
  • Connecting to Amazon Redshift using the SQL Workbench/J client
  • Connecting to Amazon Redshift using Jupyter Notebook
  • Connecting to Amazon Redshift programmatically using Python and the Redshift API
  • Connecting to Amazon Redshift using the command line (psql)
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Amazon Redshift Cookbook
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