A database is a collection of data stored in an orderly manner. 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. The same applies to a business or organization that would require the cooperative efforts of several 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 data in systematic ‘entities’ stored within different databases available. Companies can use different types of databases to meet their unique business needs.
The blog will give you an overview of databases, their types, structures, and usability. Also, it will briefly discuss the concept of a database management system (DBMS).
Definition of Database
Database is “a structured set of data held in a computer, especially one that is accessible in various ways.” There are various types of databases, each providing different functionality to their users. Here we will discuss some of the most prominent database types and examples while exploring their unique features. For instance, SQL databases do not offer a great degree of scalability to their users, and relational database schemas are stricter in nature but provide more consistency and structure.
Types of Database Objects
There are four different types of database objects that help users to compile, enter, store, and analyze data in various formats:
Why Are Databases Important?
Databases are used for keeping mountains of data collected in an orderly manner and easily accessible to an authorized user. Every company uses a different database, depending on the nature of its data. Databases are important for a business’s growth in numerous ways:
- Allows a business to make informed business decisions.
- Efficiently store and retrieve related information.
- Helps analyze and aggravate business data.
- Collect and store crucial customer data from different applications.
- Delivers data-driven timely, personalized applications and detailed analytics.
- Ensures immediate access to crucial business data that can be used by different business departments to comprehend data patterns, generate reports, and predict future trends.
- Often data is mapped through hierarchical databases used by legacy systems to relational databases used in the data warehouses.
Types of Database Users
There are various types of database (DBMS) users, such as:
- Database Administrator (DBA)
- System Analyst
- Application Programmer
- Database Designer
Types of Database Structures Explained
A company should use the type of database that aligns with its requirements and needs. There are various types of database structures:
- Hierarchical database: Hierarchical database follows a ranking order or a parent-child relationship to structure data.
- Network database: Network database is similar to the hierarchical database but with some changes. The network database allows the child record to connect with various parent records, therefore, allowing two-directional relationships.
- Object-Oriented database: In an object-oriented database, information is stored in an object-like manner.
- Relational database: A relational database is table-oriented where every bit of data has a link with every other bit of data.
- Non-relational or NoSQL database: A no SQL database uses a variety of formats, such as documents, graphs, wide columns, etc, which offers great flexibility and scalability to a database design.
Databases are widely divided into two major types or categories, namely, Relational or Sequence Databases and Non-relational or Non-sequence databases or No SQL databases. An organization may use them individually or combined, depending on the nature of data and functionality required.
Let’s delve into the various types of databases mentioned above.
A relational database is the most common type of database. It uses schema, which is a template used to dictate the data structure stored within the database.
For instance, a company selling products to its customers must have some form of stored knowledge as to where these products go, to whom, and in what quantity.
There may be different types of relational databases used for each approach. For example, the first table can be used to show basic information of customers, the second for the number of products sold, and the third table to enumerate who purchased this product and where.
There are keys associated with tables in a relational database. They provide a quick database summary or access to the particular row or column you want to check.
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 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 stored would just need to record these IDs and their quantity. Any change in these tables will affect all of them but in a predictable and systematic manner.
Some examples of SQL databases include:
Oracle database system, an Oracle Corporation product, serves as a multimodal management system.
Also called Postgre, PostgreSQL emphasizes standard compliance along with extensibility and serves as an object-relational database management system.
This particular open-source RDBMS runs on all available platforms, like, Windows, Linux, and UNIX.
A product of Microsoft, SQL Server is mainly used to store and retrieve data to and from software application systems.
Merits and Demerits of Relational Databases
Relational databases have their own merits and demerits that are worth considering before opting to invest in them:
- Relational databases follow a strict schema, meaning that each new entry must have different components that make it fit in that preformed template. It enables the data to be predictable and easily assessable.
- ACID-compliance is a must for all RDBMS databases, which means that they must ensure the provision of Atomicity, Consistency, Isolation, and Durability.
- They are well structured and significantly reduces the chances of errors.
- The meticulous nature, strict schemas, and constraints of relational databases make it nearly impossible to be stored in the numbers required 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; otherwise, it will not simply work.
Another common type of database is non-relational. Non-relational form of database organization is more forgiving in its structure and form than relational databases. Instead of tables with columns and rows, they have collections of different categories — for example, users and orders —illustrated by documents. So, there can be multiple documents in one collection. Also, they may or may not 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 is no particular schema to these documents. Also, different collections might not necessarily have relations among them.
The different types of non-relational databases are:
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 also be called a multidimensional key-value store. It stores and manages humongous amounts of data in tables or multiple columns. Each of these columns can act as a record, which helps in scaling petabytes of data. Notable examples are Scylla, HBase, and Cassandra.
Here, the uniform structure is not a necessity for records. They can have a wide array of types and values, and all of them can be nested. 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.
They are distinguished from document stores to help make the data available by simple text-based searches. Some examples are Solr, Splunk, and Exasticsearch.
Graph databases show the connections between different data points. They are used to analyze 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 the 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:
- Their schema-free nature makes it 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 several distinguished nodes for better accessibility.
- 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 detail separately.