Amazon Redshift is one of the prominent names in the field of Cloud Storage Services. It offers fully managed systems that can service the data warehousing and database needs of businesses.

Upon a complete walkthrough of this article, you will gain a decent understanding of Amazon Redshift along with the unique features that it offers.

You will also learn about Redshift Concat Commands and how to concatenate two strings in Redshift. Read along to learn more about Redshift Concat Functions!

Prerequisites

  • Basic Understanding of Redshift Commands.

Syntax and Examples of Redshift Concat Functions

Amazon Redshift primarily provides two methods to perform concatenation of strings:

1) Amazon Redshift Concat Function

  • The CONCAT Function in Redshift concatenates two strings and returns the resulting string. Redshift CONCAT Function only works with two string-type inputs.
  • Note: If the input strings are Null or even one of the input strings is Null then the resultant string after concatenation will also hold a Null value.
  • As with any other traditional function, the Redshift CONCAT Function receives arguments that refer to the input on which the concatenation is to be performed. Both arguments can be of fixed-length or have variable-length character strings or expressions. The Redshift CONCAT Function returns an output string. 

CONCAT Function Syntax

CONCAT(String 1, String 2)

Following is an example of the CONCAT function:

select concat('September 01, ', '2021');

concat
-------------------
September 01, 2021
(1 row)
  • As discussed above the Redshift CONCAT Function concatenates both the strings passed as an argument and returns a concatenated string.

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2) Concatenation Operator(||)

  • The Concatenation Operator concatenates two strings on either side of || Symbol and returns the concatenated string.
  • This method is preferable when you want to concatenate more than two strings. Similar to the Redshift CONCAT Function if one or both strings are Null then the resultant string will also be Null. 

Syntax for Concatenation Operator

select string 1 || string 2

Given below is an example of the concatenation of strings using the Concatenation Operator:

select 'Nested' || ' CONCAT' || ' example!' as sample;
         sample
------------------------
 Nested CONCAT example!
(1 row)

Redshift Null Handling Functions: Usage and Examples

  • A NULL value in a Relational Database is a special marker used in SQL to indicate that there is no data value in the database. In other words, it’s just a placeholder that indicates missing or unknown values. All Relational databases support Null Handling Functions.
  • In Amazon Redshift, Null Functions are used to handle Null values that you might receive as a part of the input of Redshift CONCAT Functions. Amazon Redshift houses the following Null Handling Functions:

1) Redshift NVL or Coalesce Function

  • An NVL expression returns the value of the first expression in the list that is not Null. The NVL Function replaces the NULL value with a replacement string that you specify as an argument in the Redshift CONCAT Function. This function returns the first argument if it is not null, otherwise the second argument.
  • This type of expression is useful when you want to return a backup value for something when the preferred value is missing or Null. For example, a query can return one of three phone numbers (cell phone, home, or work in that order), whichever is found first in the table.

Given below is the NVL function syntax:

NVL (exp,sample);

Which Returns ‘exp’ if not Null otherwise ‘sample’ will be returned.

2) Redshift NULLIF Function

  • The NULLIF Function in Redshift compares the two arguments passed as an input for the Redshift CONCAT Function. If both character strings are equal, Null is returned and if the character strings are not equal, the first argument is returned as the output.
  • Given below are some examples of  Redshift NULLIF Function:
dev=# SELECT NULLIF('B', 'B');
 case
------
NULL
(1 row)

NULL is returned as the output because both the arguments are equal.

dev=# SELECT NULLIF('A', 'B');
 case
------
 A
(1 row)

Introduction to Amazon Redshift

  • Amazon Redshift is a fully managed, Cloud-based Petabyte scale Data Warehouse developed by Amazon for the storage and analysis of datasets of mammoth scales.
  • Amazon Redshift is based on an MPP(Massively Parallel Processing) Columnar architecture and was developed to connect with numerous SQL-based Clients, Business Intelligence, and Data Visualization tools and to make data available to users in real-time. Based on PostgreSQL 8,

Key Features of Amazon Redshift

Some of the key features of Amazon Redshift are as follows:

  1. Enhanced Scalability
  2. Massively Parallel Processing (MPP)
  3. Data Lake and AWS Integrated
  4. Redshift ML
  5. Fault Tolerance
  6. Robust Security

check out their official website For further information on Amazon Redshift.

Learn More About:

Redshift NULLIF and NULL Commands

Conclusion

In this article, you learned about Amazon Redshift and the salient features that it offers. You also learned about Redshift Concat Commands and the different ways through which you can concatenate numerous strings. With your Data Warehouse, Amazon Redshift live and running,

you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.

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Share your experience of learning about Redshift Concat Commands. Tell us in the comments below!

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Former Research Analyst, Hevo Data

Rakesh is a research analyst at Hevo Data with more than three years of experience in the field. He specializes in technologies, including API integration and machine learning. The combination of technical skills and a flair for writing brought him to the field of writing on highly complex topics. He has written numerous articles on a variety of data engineering topics, such as data integration, data analytics, and data management. He enjoys simplifying difficult subjects to help data practitioners with their doubts related to data engineering.