A Quick Overview of Different Types of Databases

By |2019-06-11T07:53:08+00:00June 11th, 2019|

To run a system efficiently, you’d need an effective memory of the past and present records that went into and/or came out of that particular system. Same applies to a business or organization that would require cooperative efforts of a number of individuals. For this purpose, companies, large and small, and organizations like hospitals, schools, and universities utilize a very useful method of acquiring, assembling, and sharing the data in systematic ‘Entities’ that are stored within different types of databases.

By definition, a database is “a structured set of data held in a computer, especially one that is accessible in various ways.” It helps in keeping the mountains of data collected in an orderly manner and easily accessible to an authorized user.

Databases are widely divided into two major types, namely, Relational or Sequence Databases and Non-relational or Non-sequence databases. These may be used by an organization individually or combined, depending on the nature of the data and the functionality required.

Relational Databases

This type of database uses schema, which is a template used to dictate the structure of data that will be stored within the database.

Like for a company selling products to its customers, it is essential for them to have some form of stored knowledge as to where did these products go, to whom, and in what quantity.

There may be different tables used for each approach. For example, one table can be used to show the basic information of the customers, a second, used for the information of the products that are sold and a third table can be used to enumerate who purchased this product, how many times and where.

There are keys associated with the tables in a relational database. They help in providing quick access to the particular row or column that you might want to check.

The tables, which are also called Entities, are all in relation to each other. The table with the information about customers might provide a specific ID to each customer that can simply denote all there is to know about that customer like their address, name, and contact information. Also, the table with the product description can assign a particular ID to each product. The table where all the orders are recorded would just need to record these IDs and their quantity. Any change in any one of these tables will affect all of them but in a predictable and systematic manner.

Data integrity is ensured by constraints of the RDBMS (Relational Database Management System). An RDBMS ensures that the data that shows up is accurate and can be relied upon.

Some examples of these Structured Query Language (SQL) databases are:

Oracle

This database system, an Oracle Corporation product, serves as a multimodal management system.

PostgreSQL

Also called Postgre, PostgreSQL emphasizes on standard compliance along with extensibility and serves as an object-relational database management system.

MySQL

This particular open-source RDBMS runs on all the available platforms, like, Windows, Linux, and UNIX.

Merits and Demerits of Relational Databases

Relational databases have their own merits and demerits that are worth considering before opting to invest in them:

Merits

  • Relational databases follow are a strict schema, meaning that each new entry must have different components that make it fit in that preformed template. This enables the data to be predictable and easily assessable.
  • ACID-compliance is a must for all RDBMS databases. This means that they must ensure the provision of Atomicity, Consistency, Isolation, and Durability.
  • They are well structured and give little to no space for any kind of error to occur.

Demerits

  • The meticulous nature, strict schemas, and constraints of relational databases make it nearly impossible to be stored in the numbers that are a requirement for today’s mammoth internet data.
  • It’s impossible to scale horizontally as relational databases follow a particular schema. Although vertical scaling seems like the obvious answer, it’s not. Vertical scaling has a limit and, in this time, and age, the data collected via the internet daily is simply too large to imagine that vertical scaling would work for long.
  • Schema constraints also impede the migration of data to and from different RDBMS. They need to be identical or it will not simply work.

Non-Relational Databases

Non-relational databases are more forgiving in their structure and form than relational databases. Instead of tables with columns and rows, they have collections of different categories like users and orders, for example. These collections are illustrated by documents. So, there can be multiple documents in one collection and they can or cannot follow any particular pattern or schema.

A document can have a name, address, and product in a collection; at the same time, another document can have just a name and product in the same collection as there are no particular schema to these documents. Also, different collections might not necessarily have relations among them.

The different types of non-relational databases are:

Key-Value Stores

This type only stores and provides quick and simple knowledge regarding key-value pairs. This is a simple and easy way to store and access the data. Some examples are Amazon DynamoDB and Redis.

Wide Column Stores

This type can be also called a multidimensional key-value store as it stores and manages humongous amounts of data in tables or multiple columns, each column of which can act as a record. This helps in the scaling of multiple petabytes of data. Notable examples are Scylla, HBase, and Cassandra.

Document Stores

Here, the uniform structure is not a necessity for records to have. They can have a wide array of types and values and all of them can be nested. The data gets stored in JSON documents, and these documents resemble those of key-value and wide-column. Some of the most famous NoSQL databases fall into this category, namely, Couchbase and MongoDB.

Search Engines

They are distinguished from document stores in a way that they help in making the data available by simple text-based searches. Some examples are Solr, Splunk, and Exasticsearch.

Graph Databases

These show the connections between different data points. They are used mostly when there is a requirement of analyzing different types of data and their relationship with each other. These are represented in the form of a network of objects or nodes that are related. Examples are Datastax Enterprise Graph and Neo4J.

Merits and Demerits of Non-Relational Databases

Like everything else, non-relational databases are not perfect and have some advantages but also some limitations. These include:

Merits

  • Their schema-free nature makes them easier to manage and store huge volumes of data. They can also be easily scaled horizontally.
  • Data is not too complex and can be distributed among a number of distinguished nodes for better accessibility.

Demerits

  • Since they have no specific structure or schema for the data stored, you cannot rely on your data for a certain field because it might not have it.
  • Having no relations makes it very hard to update the data as you will have to update every single detail separately.