Running data-sensitive analysis on heterogeneous data silos or even tiny sample datasets will not deliver accurate or comprehensive insights to a company. Therefore, delivering relevant insights to an enterprise via big data analytics necessitates a cloud-based infrastructure that can store, handle, and compute large volumes of data. Cloud data warehouses fill this demand by granting access to data rather than moving data. This ensures that all company data is safe and secure.
In this article, we will learn about basic introduction to Amazon Redshift NVL and NULL functions.
The advantages of incorporating a cloud data warehouse into a data pipeline include independent storage and compute, the availability of workload-specific engines on demand, and a huge reduction in the complexity of generating and managing high-performance workloads. They also allow for the execution of queries across many repositories, as well as scalable data movement and replication.
AWS, with its Redshift service, allows companies to handle massive amounts of data; it can process petabytes of structured and semi-structured data. In addition, Redshift can be used for large-scale data migrations. Like many other AWS services, Redshift can be set up in a matter of minutes and offers a variety of data input options.
Table of Content
- Understanding of SQL
- Understanding of databases
Introduction to Amazon Redshift
Amazon Redshift is a cloud data warehouse from Amazon that was created to interact with a variety of SQL-based clients, business Intelligence, and data visualization tools to make data available to users in real-time. In comparison to other data warehouses, Amazon Redshift is based on PostgreSQL 8, providing substantially improved performance and more efficient queries. This aids teams in making and assessing data-driven business decisions quickly.
For data that are in transit, Amazon Redshift uses SSL encryption. And for data at rest, it uses hardware-accelerated AES-256 encryption.
Features of Amazon Redshift
Here are a few key features of Amazon Redshift that can elicit the importance of Amazon Redshift:
For any database or data warehouse user, data access and reliability are critical. Amazon Redshift keeps track of your clusters and nodes around the clock. In case a node or cluster fails, Redshift instantly replicates all data to another node or cluster that is fully functional.
Massive Parallel Processing Architecture
Amazon Redshift is built on the shared-nothing Massively Parallel Processing (MPP) architecture. It is made up of Data Warehouse Clusters that are divided into Leader Node and a set of Compute Nodes. The leader node compiles the code and distributes it to the cluster’s individual compute nodes. The compute nodes run the compiled code and report the intermediate results to the leader node for aggregation. Each computing node has its CPU, RAM, and disc storage attached to it. Moreover, all compute nodes are further divided into slices, each of which handles a fraction of the workload assigned to it.
In addition, Amazon Redshift has custom Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC) drivers that users can download from the Amazon Redshift console, using which they can connect to existing SQL client applications, BI tools, and data mining tools.
Customers can pick the degree of capabilities according to their peak workload times, and Redshift can scale quickly. It supports two kinds of scaling operations: classic and elastic. You can also quickly build up a cluster with Redshift by recovering data from a snapshot. This is quite effective when customers require additional computational resources to accommodate high concurrency.
Regardless of whether the data is at rest or in transit, Redshift ensures comprehensive data security. Also, by having third-party auditors test the security regularly, it ensures total security for all Amazon Redshift-related tasks, like cluster administration, cluster connectivity, database management, and credential management. All well-known data protection and security compliance procedures, such as SOC, PCI, HIPAA BAA, and others, are also supported by AWS Redshift.
Redshift gives consistent query speed for datasets spanning in size from a few gigabytes to petabytes. Its columnar storage, data compression, and zone maps techniques decrease the amount of I/O memory required for query execution. When queried using Redshift materialized views, it improves the performance of extract, transform, and load (ETL) data processing activities and allows analysts to conduct substantially faster queries.
Amazon Redshift is renowned for delivering robust data security protections at no extra charge. You can use Firewall rules in Redshift to restrict network access to a single Data Warehouse Cluster. Further, it also specializes in a granular, row, and column-level security controls, ensuring that only people with the appropriate access (Amazon Identity and Access Management (IAM)) can view data. Through Tokenization, End-to-End Encryption, Network Isolation, and Auditing, Amazon Redshift delivers on its promise of reliability and compliance.
Administrators can choose to isolate their network within Redshift if their company needs more protection. Enabling the Amazon VPC restricts network access to an organization’s cluster(s) in this circumstance. The IPsec VPN keeps the user’s data warehouse connected to the existing IT infrastructure.
The data in Amazon Redshift can be automatically and regularly scheduled to get backups directly from the new data to Amazon S3. It is capable of storing all of your snapshots for a length of time ranging from 1-35 days. You can also take snapshots at any moment by saving the deleted data. In an event of disaster recovery, Amazon Redshift can effortlessly replicate S3 snapshots.
To optimize Redshift for your data warehousing needs, Amazon provides users the option of choosing between two kinds of nodes. Using fast CPUs, massive RAM, and solid-state drives (SSD), users can build high-performance data warehouses with Dense Compute (DC) nodes. They can shift to the more cost-effective Dense Storage (DS) node types, which employ larger hard disc drives for a low price point if they wish to scale even further or save money. A simple API call or a few clicks in the Amazon Web Services Management Console is all it takes to scale cluster or alter node types.
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Null Handling Functions
In a relational database, a NULL value is a particular SQL marker that indicates that a data value does not exist in the database. In other words, it’s a substitute for values that aren’t known or are missing. Null handling functions are available in almost all relational databases. Amazon Redshift offers the following Null Handling Functions:
Redshift NVL or COALESCE
A Redshift NVL expression returns the value of the first non-null expression in the list. The Redshift NVL function substitutes a NULL value with a string that users supply as an argument to the Redshift NVL function.
The Redshift NVL function is similar to the COALESCE function in Redshift SQL. The first non-NULL value will be returned by the Redshift NVL function. The result is null if all expressions are null. The remaining expressions in the list are not evaluated until a non-null value is obtained.
NVL (expression, expression, ... );
Here, the Redshift NVL expression will return the value of the first expression in the list that is not null.
SELECT NVL(shakes, 'lemonade') FROM Beverages;
In this case, if the shakes column of the Redshift NVL function contained a NULL value, the value of ‘lemonade’ would be substituted.
The following Query will also give the same results in Google BigQuery:
SELECT COALESCE(shakes, 'lemonade') FROM Beverages;
Redshift NVL2 Function returns the second argument if the first argument is not null, otherwise, it returns the third argument. In other words, if a given expression evaluates to NULL or NOT NULL, one of the two values is returned. Redshift NVL2 is shorthand for the Redshift CASE expression with a syntax:
WHEN value THEN result
Redshift NVL2 takes three parameters: an expression, a replacement value 1, and a replacement value 2.
NVL2(exp, replacement-exp1, replacement-exp2);
NVL2 (expression, not_null_return_value, null_return_value)
Note: Both not_null_return_value and null_return_value values must have the same data type as an expression or be implicitly convertible to that data type.
Redshift NVL2 works like an IF statement. Returns ‘replacement-exp1’ if ‘exp’ is not null, otherwise ‘replacement-exp2’ is returned.
select nvl2(DOB, ‘1994’, 1996);
In Redshift NVL2, the return type is determined by the following factors:
- If either not_null_return_value or null_return_value is null, the data type of the not-null expression is returned.
If both not_null_return_value and null_return_value are not null:
- If not_null_return_value and null_return_value have the same data type, then the data type gets returned.
- If not_null_return_value and null_return_value have different numeric data types, the smallest compatible numeric data type gets returned.
- If not_null_return_value and null_return_value have different datetime data types, a timestamp data type gets returned.
- If not_null_return_value and null_return_value have different character data types, the data type of not_null_return_value gets returned.
- If not_null_return_value and null_return_value have mixed numeric and non-numeric data types, the data type of not_null_return_value gets returned.
When two arguments are equivalent, the NULLIF statement compares them and returns null. If the two arguments are not equal, the first one is returned. This is the inverse expression of the Redshift NVL or COALESCE expression.
dev=# SELECT NULLIF('Candycane', 'Candycane');
dev=# SELECT NULLIF('Candycane', 'Marshmallow');
We learned about Amazon Redshift in this post, as well as some of its key features. In addition, we also have a brief insight into NULL functions available in Redshift. This blog talks about the Amazon Redshift NVL function as well.
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