book

Amazon Redshift Cookbook

by Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel

July 2021

Intermediate to advanced

384 pages

6h 48m

English

Packt Publishing

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ForewordContributorsAbout the authorsAbout the reviewers
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Technical requirementsCreating an Amazon Redshift cluster using the AWS ConsoleGetting readyHow to do it…Creating an Amazon Redshift cluster using the AWS CLIGetting readyHow to do it…How it works…Creating an Amazon Redshift cluster using an AWS CloudFormation templateGetting readyHow to do it…How it works…Connecting to an Amazon Redshift cluster using the Query EditorGetting readyHow to do it…Connecting to an Amazon Redshift cluster using the SQL Workbench/J clientGetting readyHow to do it…Connecting to an Amazon Redshift Cluster using a Jupyter NotebookGetting readyHow to do it…Connecting to an Amazon Redshift cluster using PythonGetting readyHow to do it…Connecting to an Amazon Redshift cluster programmatically using JavaGetting readyHow to do it…Connecting to an Amazon Redshift cluster programmatically using .NETGetting readyHow to do it…Connecting to an Amazon Redshift cluster using the command lineGetting readyHow to do it…
Technical requirementsManaging a database in an Amazon Redshift clusterGetting readyHow to do it…Managing a schema in a databaseGetting readyHow to do it…Managing tablesGetting readyHow to do it…How it works…Managing viewsGetting readyHow to do it…Managing materialized viewsGetting readyHow to do it…How it works…Managing stored proceduresGetting readyHow to do it…How it works…Managing UDFsGetting readyHow to do it…How it works…
Technical requirementsLoading data from Amazon S3 using COPYGetting readyHow to do it…How it works…Loading data from Amazon EMRGetting readyHow to do it…Loading data from Amazon DynamoDBGetting readyHow to do it…How it works…Loading data from remote hostsGetting readyHow to do it…Updating and inserting dataGetting readyHow to do it…Unloading data to Amazon S3Getting readyHow to do it…
Technical requirementsIngesting data from transactional sources using AWS DMSGetting readyHow to do it…How it works…Streaming data to Amazon Redshift via Amazon Kinesis FirehoseGetting readyHow to do it…How it works…Cataloging and ingesting data using AWS GlueHow to do it…How it works…
Technical requirementsScheduling queries using the Amazon Redshift query editorGetting readyHow to do it…How it works…Event-driven applications using Amazon EventBridge and the Amazon Redshift Data APIGetting readyHow to do it…How it works…Event-driven applications using AWS LambdaGetting readyHow to do it…How it works…Orchestrating using AWS Step FunctionsGetting readyHow to do it…How it works…Orchestrating using Amazon MWAAGetting readyHow to do it…How it works…
Technical requirements Managing infrastructure securityGetting readyHow to do itData encryption at restGetting readyHow to do itData encryption in transitGetting readyHow to do itColumn-level securityGetting readyHow to do itHow it worksLoading and unloading encrypted dataGetting readyHow to do itManaging superusersGetting readyHow to do itManaging users and groupsGetting readyHow to do itManaging federated authenticationGetting readyHow to do itHow it worksUsing IAM authentication to generate database user credentialsGetting readyHow to do itManaging audit logsGetting readyHow to do itHow it worksMonitoring Amazon RedshiftGetting readyHow to do itHow it works
Technical requirementsAmazon Redshift AdvisorGetting readyHow to do it…How it works…Managing column compressionGetting readyHow to do it…How it works…Managing data distribution Getting readyHow to do it…How it works…Managing sort keys Getting readyHow to do it…How it works…Analyzing and improving queriesGetting readyHow to do it…How it works…Configuring workload management (WLM)Getting readyHow to do it…How it works…Utilizing Concurrency ScalingGetting readyHow to do it…How it works…Optimizing Spectrum queriesGetting readyHow to do it…How it works…
Technical requirementsAWS Trusted AdvisorGetting readyHow to do it…How it works…Amazon Redshift Reserved Instance pricingGetting readyHow to do it…Configuring pause and resume for an Amazon Redshift clusterGetting readyHow to do it…Scheduling pause and resumeGetting readyHow to do it…How it works…Configuring Elastic Resize for an Amazon Redshift cluster Getting readyHow to do it…Scheduling Elastic Resizing Getting readyHow to do it…How it works…Using cost controls to set actions for Redshift SpectrumGetting readyHow to do it…Using cost controls to set actions for Concurrency ScalingGetting readyHow to do it…

Technical requirementsBuilding a data lake catalog using AWS Lake FormationGetting readyHow to do it…How it works…Exporting a data lake from Amazon RedshiftGetting readyHow to do it…Extending a data warehouse using Amazon Redshift SpectrumGetting readyHow to do it… Data sharing across multiple Amazon Redshift clustersGetting readyHow to do it…How it works…Querying operational sources using Federated QueryGetting readyHow to do it…
Technical requirementsManaging Amazon Redshift ML Getting readyHow to do it…How it works…Visualizing data using Amazon QuickSightGetting readyHow to do it…How it works…AppFlow for ingesting SaaS data in RedshiftGetting readyHow to do it…How it works…Data wrangling using DataBrewGetting readyHow to do it…How it works…Utilizing ElastiCache for sub-second latencyGetting readyHow to do it…How it works…Subscribing to third-party data using AWS Data Exchange Getting readyHow to do it…How it works…
Recipe 1 – Creating an IAM userRecipe 2 – Storing database credentials using Amazon Secrets ManagerRecipe 3 – Creating an IAM role for an AWS serviceRecipe 4 – Attaching an IAM role to the Amazon Redshift cluster
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Content preview from Amazon Redshift Cookbook

Chapter 9: Lake House Architecture

The lake house is an architectural pattern that makes data easily accessible across customers' analytics solutions, thereby preventing data silos. Amazon Redshift is the backbone of the lake house architecture—it allows enterprise customers to query data across data lakes, operational databases, and multiple data warehouses to build an analytics solution without having to move data in and out of these different systems. In this chapter, you will learn how you can leverage the lake house architecture to extend the data warehouse to services outside Amazon Redshift to build your solution, while taking advantage of the built-in integration. For example, you can use the Federated Query capability to join the operational ...