The functionality that databases offer to users is largely dependent on their design. Since data is a dynamic object, there are many different ways it can be stored, and therefore businesses create database systems to meet their own requirements.
The structure of each database type has a significant impact on the functionality that they provide to users. Data can be stored in a variety of ways since it is a dynamic item. In order to satisfy their own specific needs, businesses develop their own databases system.
This section will provide an overview of the most commonly used databases, including their different forms, structures, and functionality.
Although data comes in a wide variety of shapes and sizes, it can generally be categorized as either structured, unstructured or semi-structured data.
Structured Data - Structured data is data that is in a standardized format, has a well-defined structure, follows a consistent order, and is easily accessed by humans and programs. Usually, a database is used to store this type of data.
Unstructured Data - Unstructured data can take on any number of different forms and lacks any pre-established formation. Unstructured data examples include graphics and text files such as PDF documents, as well as video and audio files, to mention a few.
Semi-Structured Data - Semi-structured data, sometimes referred to as partially-structured data, is data that lacks the tabular organization common to relational databases and other types of data tables but still has tags and metadata to distinguish semantic components and create hierarchies of records and fields.
Companies aim to use databases that will assist them in managing complex data, using two different types of management systems: relational and non-relational databases. Non-relational databases, also known as NoSQL databases, store semi-structured or unstructured data, whereas relational databases are structured. In contrast to non-relational databases, which store data in graphs and documents, relational databases (SQL) consist of tables (fields and records) (XML, JSON).
A relational database, also known as a Relational Database Management System (RDBMS) or SQL (Structure Query Language) database, is a collection of data items with pre-established relationships between them.
Data items in relational databases are organized as a series of tables with columns and rows. A field keeps the actual value of an attribute, and each column in a table holds a specific type of data. The table's rows stand for a group of connected values for a single object or entity. A primary key, which serves as a distinctive identifier for every row in a table, can be used to link rows from different tables together.
Over the years, several free variations of these RDBMS platforms have gained prominence, including SQL Server Express, PostgreSQL, SQLite, MySQL, and MariaDB.
The non-relational database, often known as a NoSQL database, holds data, but unlike relational database, this data is stored without any tables, rows, or primary keys. Instead, the non-relational database employs a storage architecture tailored to the particular needs of the sort of data being stored.
Over the years, several NoSQL databases grew in popularity, including MongoDB, Apache Cassandra, Redis, Couchbase, and AWS DocumentDB.
Non-relational databases are classified into four most common types: document data stores, column-oriented databases, key-value stores, and graph databases. Frequently, a single application will use a combination of these categories.
Document databases - A document database controls a collection of named string fields and object data values in an entity known as a "document." Document databases offer a large deal of flexibility by not requiring that all documents maintain the same data structures. Documents are commonly saved as JSON files, which can be encoded using a number of different methods, including XML, YAML, JSON, BSON, or plain text.
Column-oriented databases - Wide-column databases, also known as column family databases, are a particular kind of NoSQL database in which the names and formats of the columns can differ between rows, even within the same table. The fact that data is organized into columns makes it possible to search and load the full column rapidly when a query is made for a specific value in a column. Examples include Apache Cassandra, ScyllaDB and Apache Parquet.
Graph databases - These are the most complicated non-relational databases, created to effectively store relationships between entities. Graph databases are an excellent solution when data is heavily interrelated, such as in purchasing and manufacturing systems or catalogs used for reference. Examples include FlockDB and GraphDB.
Key-value databases - The key-value store is the simplest NoSQL database and, as its name suggests, is only a collection of key-value pairs stored within an object. Examples include Redis, Amazon DynamoDB and Oracle NoSQL database.