The popularity of the Cloud as a Data Storage Platform has grown manifolds as individuals and businesses have recognized the benefits of storing their data in the Cloud Storage Systems. For this reason, most companies have moved their data from On-Premise Databases to Cloud Storage Systems. The choice of the data carrier is of crucial importance for any organization, as it determines the speed at which queries are processed and the costs involved. Cloud Storage Systems are cheaper for data storage because they don’t burden their users with the responsibility of Server Maintenance.
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!
Table of Contents
- Basic Understanding of Redshift Commands.
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, Amazon Redshift offers significantly enhanced performance and more efficient queries compared to all other Data Warehouses. This helps teams make data-driven business decisions and Analysis. More than 15,000 companies worldwide now use Amazon Redshift, including large companies such as Pfizer, McDonald’s, Facebook, etc.
Key Features of Amazon Redshift
Some of the key features of Amazon Redshift are as follows:
- Enhanced Scalability: Amazon Redshift is known for providing fast and consistent performance, even with thousands of concurrent queries. Amazon Redshift’s Concurrent Scaling supports nearly unlimited concurrent users and queries.
- Massively Parallel Processing (MPP): Massively Parallel Processing is a distributed design approach in which multiple processors use a Divide-and-Conquer strategy on large datasets. A large processing job is broken down into smaller jobs, which are then distributed to a group of computation nodes. These nodes perform their computations in parallel instead of one after the other. As a result, the time it takes Redshift to complete a single large job is dramatically reduced.
- Data Lake and AWS Integrated: Amazon Redshift enables you to work with data in a variety of open formats which can be easily integrated into the AWS ecosystem. Amazon Redshift makes it exceptionally easy to query data in open formats like JSON, ORC, CSV, Avro to name a few, and write it to your Data Lake. With the AWS Analytics ecosystem, you can effortlessly handle end-to-end Analysis Workflow. You can also bring data from various applications such as Google Analytics, Facebook Ads, Salesforce optimized into an Amazon Redshift Data Warehouse.
- Redshift ML: Amazon Redshift hosts a feature called Redshift ML that enables Data Analysts and database developers to seamlessly build, train, and deploy Amazon SageMaker models using SQL.
- Fault Tolerance: Data access and Reliability are paramount to any Database or Data Warehouse user. Amazon Redshift monitors your Clusters and Nodes 24 hours a day. If a Node or Cluster fails, Amazon Redshift automatically replicates all the data into a fully functional Node or Cluster.
- Robust Security: Amazon Redshift is known for providing robust data security features at no additional cost. With Amazon Redshift, you can configure Firewall rules to take control of network access to a specific Data Warehouse Cluster. Amazon Redshift also specializes in Granular, Row, and Column-level security controls that ensure users can only view data with the correct type of access. Amazon Redshift lives up to its promise of reliability and compliance through Tokenization, End-to-End Encryption, Network Isolation, and Auditing.
For further information on Amazon Redshift, click here to check out their official website.
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Redshift Concat Functions: Syntax and Examples
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');
September 01, 2021
As discussed above the Redshift CONCAT Function concatenates both the strings passed as an argument and returns a concatenated string.
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;
Nested CONCAT example!
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:
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');
NULL is returned as the output because both the arguments are equal.
dev=# SELECT NULLIF('A', 'B');
The first argument is returned as the output as both of the character strings are different.
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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