A database is a collection of data stored in an orderly manner. To run a system efficiently, you’d need an adequate memory of the past and present records that went into and came out of that particular system. The same applies to a business or organization requiring several individuals’ cooperative efforts. For this purpose, large and small companies and organizations like hospitals, schools, and universities utilize a beneficial 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).
What is a Database?
It is essential to understand what databases are. A database is “a structured set of data held in a computer, especially one accessible in various ways.” There are various databases, each providing different functionality to their users. We will discuss significant database types and examples while exploring their unique features here. For instance, SQL databases do not offer great scalability to their users, and relational database schemas are stricter but provide more consistency and structure.
Types of Database Objects
Four different types of database objects help users to compile, enter, store, and analyze data in various formats:
Why Are Databases Important?
Users employ databases to maintain large volumes of data in an organized manner, making it easily accessible to authorized users. Every company uses a different database, depending on the nature of its data. Databases are essential 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 different departments can use 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
What are the Different Types of Databases?
A company should use a database that aligns with its requirements and needs. There are various types of database structures:
Hierarchical database follows a ranking order or a parent-child relationship to structure data.
The database is similar to a hierarchical database but has some changes. The network database connects the child record with various parent records, allowing two-directional relationships.
In an object-oriented database, the system stores information in an object-like manner.
A relational database is table-oriented, where every bit of data is linked to 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 excellent flexibility and scalability to a database design.
Databases are divided into two major types or categories: 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 the 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, a template, 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 of 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 show essential customer information, the second for the number of products sold, and the third enumerates 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 row or column you want to check.
Tables, also called entities, are all related to each other. The table with the customer information 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 need to record these IDs and their quantity. Any change in these tables will affect all of them but predictably and systematically.
Some examples of SQL databases include:
- SQL Server
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 each new entry must have different components that 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 they must ensure the provision of Atomicity, Consistency, Isolation, and Durability.
- They are well structured and significantly reduce the chances of errors.
- The exact nature, strict schemas, and constraints of relational databases make storing the numbers required for today’s mammoth internet data nearly impossible.
- 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 too large to imagine that vertical scaling would work for long.
- Schema constraints also impede data migration to and from different RDBMS. They need to be identical; otherwise, it will not simply work.
Another common type of database is non-relational. The 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 straightforward 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 optional for records. They can have a wide array of types and values, all of which can be nested. Data gets stored in JSON documents resembling those of key-value and wide-column. Some of the most famous NoSQL databases, namely, Couchbase and MongoDB, fall into this category.
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 as a network of related objects or nodes. Examples are the Datastax Enterprise Graph and Neo4J.
Merits and Demerits of Non-Relational Databases
Like everything else, non-relational databases could be better and have advantages and limitations. These include:
- Their schema-free nature makes managing and storing vast volumes of data easier. 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 particular 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.
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