Understanding the Amazon Redshift Boolean Data Type Made Easy 101

on Amazon Redshift, Data Warehouses, Redshift Commands, SQL, Tutorials • March 11th, 2022

Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools.

The data types in Amazon Redshift are very similar to those in standard Relational Databases. Boolean is one of the most popular and often used data types. The Redshift Boolean data type is used to store logical Boolean values that can be either True or False. Though the Redshift Boolean data type appears simple to implement in your database, it can be extremely confusing if not approached the right way.

In this article, you will gain information about Amazon Redshift Boolean Datatype. You will also gain a holistic understanding of Amazon Redshift, its key features, prerequisites before working with Amazon Redshift Boolean Data type, using Amazon Redshift Boolean Data type along with examples.. Read along to find out in-depth information about Amazon Redshift Boolean Data type.

Table of Contents

What is Amazon Redshift?

Redshift Boolean Data Type - Redshift Logo
Image Source

Amazon Web Services (AWS) is a subsidiary of Amazon saddled with the responsibility of providing a cloud computing platform and APIs to individuals, corporations, and enterprises. AWS offers high computing power, efficient content delivery, database storage with increased flexibility, scalability, reliability, and relatively inexpensive cloud computing services.

Amazon Redshift, a part of AWS, is a Cloud-based Data Warehouse service designed by Amazon to handle large data and make it easy to discover new insights from them. Its operations enable you to query and combine exabytes of structured and semi-structured data across various Data Warehouses, Operational Databases, and Data Lakes.

Amazon Redshift is built on industry-standard SQL with functionalities to manage large datasets, support high-performance analysis, provide reports, and perform large-scaled database migrations. Amazon Redshift also lets you save queried results to your S3 Data Lake using open formats like Apache Parquet from which additional analysis can be done on your data from other Amazon Web Services such as EMR, Athena, and SageMaker.

For further information on Amazon Redshift, you can follow the Official Documentation.

Key Features of Amazon Redshift

The key features of Amazon Redshift are as follows:

1) Massively Parallel Processing (MPP)

Massively Parallel Processing (MPP) is a distributed design approach in which the divide and conquer strategy is applied by several processors to large data jobs. A large processing job is broken down into smaller jobs which are then distributed among a cluster of Compute Nodes. These Nodes perform their computations parallelly rather than sequentially. As a result, there is a considerable reduction in the amount of time Redshift requires to complete a single, massive job.

2) Fault Tolerance

Data Accessibility and Reliability are of paramount importance for any user of a database or a Data Warehouse. Amazon Redshift monitors its Clusters and Nodes around the clock. When any Node or Cluster fails, Amazon Redshift automatically replicates all data to healthy Nodes or Clusters.

3) Redshift ML

Amazon Redshift houses a functionality called Redshift ML that gives data analysts and database developers the ability to create, train and deploy Amazon SageMaker models using SQL seamlessly.

4) Column-Oriented Design

Amazon Redshift is a Column-oriented Data Warehouse. This makes it a simple and cost-effective solution for businesses to analyze all their data using their existing Business Intelligence tools. Amazon Redshift achieves optimum query performance and efficient storage by leveraging Massively Parallel Processing (MPP), Columnar Data Storage, along with efficient and targeted Data Compression Encoding schemes.

Simplify Redshift ETL and Analysis with Hevo’s No-code Data Pipeline

A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate and load data from 100+ Data Sources (including 40+ Free Sources) to a Data Warehouse such as Amazon Redshift or Destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line. 

Get Started with Hevo for Free

Check out some of the cool features of Hevo:

  • Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
  • Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, Databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.  
  • Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
  • 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (including 40+ free sources) that can help you scale your data infrastructure as required.
  • 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!

Prerequisites for the Using Redshift Boolean Data Type

To get started on the Redshift boolean data type, below are the basic things you need to have set up:

  • An Amazon Redshift account
  • Install a SQL client
  • Launch a Redshift cluster
  • Connect your SQL client to a database in your Redshift cluster

Getting Started with Redshift Boolean Data Type

You must first set up your database, user, and table before you can use the Redshift Boolean data type.

1) Creating your Database 

Create your database when you’ve verified that your cluster is up and functioning. This is the database where you create tables, load your data, and execute queries. An important aspect of Redshift is that you can host multiple databases on one cluster. 

Redshift Boolean Data Type: Database
Image Source: enot-poloskun | Pixabay

For example: You can run the following command in your SQL tool to create a database named “CUSTOMERDB”.

CREATE DATABASE CUSTOMERDB;

2) Creating a User 

You can create a user for your database using the command, “CREATE USER“. You will provide the new user a name and a password when you create a new user. It is necessary that you give the user a password. The password must be between 8 and 64 characters long, and it must contain at least one lowercase, one uppercase, and one numeric.

For Example: To create a user with the name “VISITOR” and password “VICy0987”, run the following command.

CREATE USER VISITOR PASSWORD ' VICy0987';

You can the same password you used to create the VISITOR user to connect to the CUSTOMERDB database.

3) Create Tables

After you have created your new database, you need to create tables to hold your data. You can use the Redshift Create Table command to add new tables to your Redshift instance. Note that the table and columns must be given a name, as well as their data types.

Redshift Boolean datatype: Creating Tables
Image Source: mcmurry julie | Pixabay

Creating a table in Redshift can be created in various ways. Here are the three main variations:

I) Create a Table (Default Method)

This command creates a new table. You have the freedom to select the table name, column names, and data types for each column. 

Run the following command:

CREATE TABLE tables_name (
columns_name1 data_type1,
columns_name2 data_type2
columns_name3 data_type3
)
Redshift Boolean Datatype: Create Table
Image Source

II) Create a Table Using AS

This table variation allows you to create a new table using the SELECT command to select data from another table. The old table’s data, column names, and data types are copied to the new one.

Using the AS clause/parameter, you can inherit the table structure and data from the SQL query. However, this command doesn’t enable inheriting constraints, default values, and other parameters.

Run the following command:

CREATE TABLE AS tables_name SELECT * from old_table
or
CREATE TABLE tables_name AS Select * from old_table;

The AS clause/parameter can be used with the Redshift Create Table command in the former way.

II) Create a Table Using LIKE

The LIKE command allows you to designate a separate table whose structure can be copied without the data being copied.

Run the following command:

CREATE TABLE tables_name LIKE previous_tables_name

If you want to use this function, you must include the LIKE clause and the parent table name in your command. The INCLUDING DEFAULTS clause can also be used to inherit default values.

Run the following command to use the INCLUDING DEFAULTS command:

CREATE TABLE tables_name (LIKE previous_tables_name INCLUDING DEFAULTS);

After successfully creating your table using any of the methods mentioned above, now you can implement the Redshift Boolean data type into the table.

Using the Redshift Boolean Data Type 

A Redshift Boolean data type is a byte column that stores “True” or “False” values. To indicate a True value in your input, you can use the characters ‘1’, ‘t’,’ y’, ‘yes’, ‘true’, or ‘TRUE‘. False values can be represented in the input as ‘0’, ‘f’, ‘n’, ‘no’, ‘false’ or ‘FALSE‘.

Redshift Boolean Data Type
Source: Borka Kiss | Pixabay

The table below lists the three possible states for a Boolean value, as well as the literal values that correspond to each state.

StateValuesStorage
False‘f’ ‘false’ ‘n’ ‘no’ ‘0’ FALSE1 byte
True‘t’ ‘true’ ‘y’ ‘yes’ ‘1’ TRUEI byte
UnknownNULL1 byte

Redshift Boolean Data Type Examples

The following are some samples of how you can use the Redshift Boolean data type in various ways. A users table has been created for the following queries.

1) Determining User’s Choice

You can use the Redshift Boolean data type to select users from the USERS table who like running but do not like jumping:

select fname, lname, likerunning, likejumping
from users
where likerunning is true and likejummping is false 
order by userid limit 5;

Output:

fnameLnamelikerunningLikejumping
LarsRatlifftF
MufutauWaltkinst
CharlieScotttF
WinifredMayerf
(5 rows)

The query in this example chooses users from the USERS table who enjoy running but not jumping.

2) Using the IS Comparison 

You can only use the IS comparison as a base in the WHERE clause to check a Boolean value.

In the following example, you can select users from the USERS table whose preference for RnB music is unknown.

select ftname, lname, likernd
from users
where likernb is unknown
order by userid limit 10;

Output:

fnamelnamelikernb
MufutauWaltkins
CharlieScott
WinifredMayer
JamesCorbin
Thomas Anthony
SamanthaRice
GraceWashington
VanessaClinton

Here is the result:

select ftname, lname, likernb is true as "check"
from users
order by userid limit 9;
[Amazon] (500453) Invalid operation: Not implemented

The following command generated an error because an IS comparison was used in the SELECT list.

You can implement it this way

select ftname, tname, likernb = true as "check"
from users
order by userid limit 9;

Output:

fnamelnamelikernb
LarsRatlifftrue
MufutauWaltkins
WinifredMayer
JamesCorbintrue
Thomas Anthony
RichardCyphertrue
SamanthaRicefalse
GraceWashington
VanessaClintontrue

The query was successful because an equal comparison (=) was used in the SELECT list instead of an IS comparison.

NOTE: You must ensure that you always check the Boolean values clearly to avoid mistakes or errors.

For further information on Amazon Redshift Boolean Data type, you can visit here.

Conclusion

In this article, you have learned about Amazon Redshift Boolean Datatype. This article also provided information on Amazon Redshift, its key features, prerequisites before working with Amazon Redshift Boolean Data type, using Amazon Redshift Boolean Data type along with examples.

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.

Visit our Website to Explore Hevo

Hevo Data with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice such as Amazon Redshift but also transform & enrich your data to make it analysis-ready. Hevo also allows integrating data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Amazon Redshift Boolean Data Type in the comment section below! We would love to hear your thoughts.

Data Engineering
Survey 2022
Calling all data engineers – fill out this short survey to help us build an industry report for our data engineering community.
TAKE THE SURVEY
Amazon Gift Cards of $25 each are on offer for all valid and complete survey submissions.