Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions , Second Edition

Shruti Worlikar

Patel

Anusha Challa

$49.99

Paperback Apr 2025 468 pages 2nd Edition

Shruti Worlikar

Patel

Anusha Challa

$49.99

Paperback Apr 2025 468 pages 2nd Edition

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Amazon Redshift Cookbook

Data Management

Amazon Redshift is a data warehousing service optimized for online analytical processing (OLAP) applications. You can start with just a few hundred gigabytes (GB) of data and scale to a petabyte (PB) or more while maintaining high performance and cost-effectiveness. Its columnar storage architecture is specifically designed for large-scale analytics workloads, storing data by column rather than row. This approach is particularly efficient for OLAP operations as it allows Redshift to read only the specific columns needed for analysis, significantly improving query performance for operations like aggregations, trend analysis, and historical data comparisons. The columnar design also enables better data compression since similar data types are stored together, reducing storage costs and enhancing input/output (I/O) performance. By designing your database to take advantage of this columnar architecture, you can maximize the efficiency of your analytical processing workloads...

Managing a database in Amazon Redshift

Amazon Redshift consists of at least one database, and it is the highest level in the namespace hierarchy for the database objects. This recipe will guide you through the steps needed to create and manage a database in an Amazon Redshift data warehouse.

Getting ready

To complete this recipe, you will need everything mentioned in the Technical requirements section at the start of the chapter.

How to do it…

Let’s now set up and configure a database on Amazon Redshift. Use the SQL client to connect to it and execute the following commands:

We will create a new database called qa in the Amazon Redshift data warehouse that has the owner awsuser and accepts a maximum of 50 connections. To do this, use the following code:
```
CREATE DATABASE qa
WITH
OWNER awsuser
CONNECTION LIMIT 50;
```

OWNER and CONNECTION LIMIT are optional parameters. You can find the complete list of options available for...

Managing tables in a database

In Amazon Redshift, you can create a collection of tables within a schema with related entities and attributes. Working backward from your business requirements, you can use different modeling techniques to create tables in Amazon Redshift. You can choose a star or snowflake schema by using normalized, denormalized, or data vault data modeling techniques.

In this recipe, we will create tables in the finance schema, insert data into those tables, and cover the key concepts to leverage the massively parallel processing (MPP) and columnar architecture.

Getting ready

To complete this recipe, you will need everything mentioned in the Technical requirements section at the start of the chapter.

How to do it…

Let’s explore how to create tables in Amazon Redshift:

Let’s create a customer table in the finance schema with the customer_number, first_name, last_name, and date_of_birth attributes:
```
CREATE TABLE...
```

Managing views in a database

View database objects allow the result of a query to be stored. In Amazon Redshift, views run each time the view is mentioned in a query. The advantage of using a view instead of a table is that it can allow access to only a subset of data on a table, join more than one table into a single virtual table, and act as an aggregated table, and it takes up no space on the database since only the definition is saved, hence making it convenient to abstract complicated queries. In this recipe, we will create views to store queries for the underlying tables.

Getting ready

To complete this recipe, you will need everything mentioned in the Technical requirements section at the start of the chapter.

How to do it…

Let’s create a view using the CREATE VIEW command. We will use the following steps to create a view:

Create a finance.customer_vw view based on the results of the query on finance.customer:
```
CREATE VIEW finance...
```

Download Code

Key benefits

Learn how to translate familiar data warehousing concepts into Redshift implementation
Use impressive Redshift features to optimize development, productionizing, and operation processes
Find out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queries
Purchase of the print or Kindle book includes a free PDF eBook

Description

Amazon Redshift Cookbook offers comprehensive guidance for leveraging AWS's fully managed cloud data warehousing service. Whether you're building new data warehouse workloads or migrating traditional on-premises platforms to the cloud, this essential resource delivers proven implementation strategies. Written by AWS specialists, these easy-to-follow recipes will equip you with the knowledge to successfully implement Amazon Redshift-based data analytics solutions using established best practices. The book focuses on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. The book covers recipes to help you take full advantage of Redshift's columnar architecture and managed services. You'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, helping you minimize the operational effort that you invest in managing regular ETL pipelines and ensuring the timely and accurate refreshing of your data warehouse. By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution by adopting best-practice approaches for solving commonly faced problems.

Who is this book for?

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

What you will learn

Integrate data warehouses with data lakes using AWS features
Create end-to-end analytical solutions from sourcing to consumption
Utilize Redshift's security for strict business requirements
Apply architectural insights with analytical recipes
Discover big data best practices for managed solutions
Enable data sharing for data mesh and hub-and-spoke architectures
Explore Redshift ML and generative AI with Amazon Q

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Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions , Second Edition

What do you get with Print?

Amazon Redshift Cookbook

Data Management

Technical requirements

Managing a database in Amazon Redshift

Getting ready

How to do it…

Managing a schema in a database

Getting ready

How to do it…

Managing tables in a database

Getting ready

How to do it…

Managing views in a database

Getting ready

How to do it…

Managing materialized views in a database

Getting ready

How to do it…

Managing stored procedures in a database

Managing UDFs in a database

Getting ready

Page 1 of 9

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Table of Contents

Recommendations for you

About the 3 authors

FAQs

Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions , Second Edition

What do you get with Print?

Contact Details

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Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Contact Details

Shipping Address

Billing Address

Product Details

Packt Subscriptions

Table of Contents

Recommendations for you

About the 3 authors

FAQs

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