Operational Databases

PostgreSQL Relational Database

The most extensively used open source relational database is PostgreSQL. It was created at the University of California, Berkeley, and has been an active open source project for almost 15 years.

Many functionality found solely in proprietary RDBMSs are also supported by PostgreSQL, including the following:

• Capability to directly handle “objects” within the relational schema
• Foreign keys (referencing keys from one table in another)
• Triggers (events used to automatically start a stored procedure)
• Complex queries (subqueries and joins across discrete tables)
• Transactional integrity
• Multiversion concurrency control

The extensibility of PostgreSQL is its true strength. Users and database programmers can add additional features to the database without impacting its basic function or reliability. Extensions possible:

• Data types
• Operators
• Functions
• Indexing methods
• Procedural languages

Let us see an example of extension of PostgreSQL features by adding user-defined data types.

User Defined Data Types in PostgreSQL

In addition to the built-in data types, PostgreSQL allows you to establish your own data types using the following statement:

• CREATE DOMAIN produces a user-defined data type where you can add constraints like NOT NULL, CHECK, etc.

Let us see an example to understand it better.

Example

Statements given below create a table namely students with the mentioned column_names and constraints:

CREATE TABLE students (
    id SERIAL PRIMARY KEY,
    first_name VARCHAR NOT NULL,
    last_name VARCHAR NOT NULL,
    roll_no VARCHAR NOT NULL,
    CHECK (
        first_name !~ '\s'
        AND last_name !~ '\s'
    )
);

As you can observe, first_name and last_name columns cannot accept null or spaces as their values. You can construct a full_name domain and reuse it in various columns instead of establishing the CHECK constraint.

The following sentence uses the CREATE DOMAIN command to create a new domain called full_name using the VARCHAR data type and no NULLs or spaces:

CREATE DOMAIN full_name AS 
   VARCHAR NOT NULL CHECK (value !~ '\s');

And you can now use full_name as the datatype of the first_name and last_name columns as a custom built-in type:

CREATE TABLE student (
    id serial PRIMARY KEY,
    first_name full_name,
    last_name full_name,
    roll_no VARCHAR NOT NULL
);

Other statements used to insert, delete, update the records of this table are exactly the same. Thus, we can see that this extensibility can be used in a variety of different scenarios and it can reduce the code complexity to a great extent.

Non-Relational Databases

Non-Relational databases do not use the table/key model found in relational databases. In the Big Data era, specialist data necessitates specialty persistence and data manipulation tools. Although the basics of relational and nonrelational databases are similar, the way the fundamentals are implemented differs. The following properties are shared by Nonrelational database technologies:

• Scalability: In this case, we're talking about the ability to write data to numerous data stores at the same time, regardless of the underlying infrastructure's physical restrictions. Seamlessness is another crucial factor to consider. Databases must be able to expand and contract in response to data flows while remaining completely invisible to end users.
   
• Data & Query Model: Nonrelational databases employ speciality frameworks to store data. They process data intelligently using speciality query APIs instead of the row, column, structure of relational databases.
   
• Persistence Design: In Nonrelational databases, persistence is still a critical component. Because of the tremendous pace, variety, and amount of big data, these databases employ a variety of data persistence strategies. The highest performance option is "in memory," which keeps the entire database in your servers' extremely fast memory system.
   
• BASE Principle: While relational databases utilize ACID (Atomicity, Consistency, Isolation, and Durability) to ensure data consistency, non-relational databases use BASE: Basically Available, Soft State, and Eventual Consistency. The most significant of these is eventual consistency, which is responsible for conflict resolution when data is moving between nodes in a distributed system. The programme maintains the data state, and the access paradigm is based on basic availability.

Key-Value Pair (KVP) Databases

The Key-Value Pair (KVP) database model is by far the most basic of the NoSQL databases. KVP databases, unlike RDBMSs, do not require a schema and provide excellent flexibility and scalability. Because KVP databases lack ACID (Atomicity, Consistency, Isolation, and Durability) functionality, implementers must consider data location, replication, and fault tolerance, which are not explicitly regulated by the technology. The databases in KVP aren't typed. As a result, the majority of the information is saved as strings.

Example

The table given below shows how key-value pairs are stored in KVP databases. It shows the favorite company choices of social media platform users.

Keeping track of precise keys and related values becomes more difficult as the number of users grows. The number of key-value pairs linked with millions of users can grow exponentially if you need to keep track of their thoughts. If you don't want to limit your value options, KVP's generic string representation offers freedom and readability.

In a key-value database, you might need some extra help organising data.

The majority of them allow you to create a collection of keys (and their associated values). Collections can be made up of any number of key-value pairs and do not necessitate exclusive control over the individual KVP elements.

RIAK Key-Value Database

One widely used open source key-value pair database is called Riak. It is developed and supported by a company called Basho Technologies (www.basho.com) and is made available under the Apache Software License v2.0.

Riak is a key-value database system that is both quick and scalable. Because it is lightweight, it can handle a high-volume environment with rapidly changing data. Riak is particularly good in real-time financial services trade analysis. It employs "buckets" as an organizing technique for key and value collections. 

Riak implementations are peer-to-peer networks made up of physical or virtual nodes. Because there is no master node, the cluster is very scalable and resilient. The cluster stores and distributes all data and actions. The performance profile of Riak clusters is intriguing. Larger clusters (with more nodes) outperform smaller clusters in terms of performance and speed. A specific protocol called Gossip is used to communicate within the cluster.

Features

• Parallel Processing: Riak has the ability to decompose and recompose queries throughout the cluster for real-time analysis and computation using MapReduce.
   
• Link Walking: Riak may be built to look like a graph database using links and link walking. A link between key-value pairs can be thought of as a one-way connection. Walking (following) the connections will reveal a map of key-value pair associations.
   
• Search: Riak Search is a distributed full-text search engine that is fault-tolerant. Buckets can be indexed for quick value resolution to keys.
   
• Secondary Indexes: Developers can use secondary indexes to tag values with one or more key field values. After that, the application can query the index and get a list of keys that match. This can be very useful in big data implementations because the operation is atomic and will support real-time behaviors.

FAQs

What does an operational database do?

An operational database is a database that stores data inside of an enterprise. They can contain things like payroll records, customer information and employee data.

Why is the purpose of operational databases and data warehouses different?

An operational database query allows to read and modify operations, while an OLAP query needs only read only access to stored data. An operational database maintains current data. On the other hand, a data warehouse maintains historical data. focuses on modeling and analysis of data for decision making.

Is operational databases volatile?

Data within operational systems are mainly updated regularly according to need. Non-volatile, new data may be added regularly. Once Added rarely changed. It is designed for real-time business dealing and processes.

Conclusion

In this article we have extensively discussed the concept of operational databases in detail. We understood the concept of relational and non-relational databases and studied their features in detail. We also saw examples in each case to grasp the topics better.

We hope that this blog has helped you enhance your knowledge regarding Operational Databases and if you would like to learn more, check out our articles on Big Data, Hadoop and Databases in general. Do upvote our blog to help other ninjas grow. 

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