What Is DBMS (Database Management System)? Definition, Types, Properties, and Examples

Key to powering correlations, analysis, and data-driven workflows, DBMS helps store, manage, and scale data efficiently.

Last Updated: August 18, 2022

A database management system – abbreviated as DBMS – is a computerized solution that helps store information in a manner that is easy to read, edit, delete, and scale, with the primary objective of drawing correlations, powering analysis, and supporting data-driven workflows. It has a backend storage system as well as a front-end user interface. This article explains the working of DBMS, along with key examples. 

What Is DBMS (Database Management System)?

A database management system – abbreviated as DBMS – is defined as a computerized solution that helps store information in a manner that is easy to read, edit, delete, and scale, with the primary objective of drawing correlations, powering analysis, and supporting data-driven workflows. 

A database management system (or DBMS) is a computerized data-keeping system. System users can carry out various actions on such a system to manipulate the data and manage the database structure.

Data was originally arranged in disparate file formats. At the time, DBMS was a novel idea, and extensive research was conducted to enable it to overcome the limitations of the conventional approach to data management. Data structures or types classify database management systems (DBMSs).

A DBMS often alters the data itself, the format of the data, the field names, the record’s structure, and the file’s structure. It also specifies guidelines for manipulating and validating this data. As database administration techniques evolve, specific data handling principles are used.

Earlier, databases could only manage distinct, individually prepared chunks of data. Today’s sophisticated systems can handle various types of less structured data and connect them in more complex ways. End users can now create, protect, read, update, and remove data in a database with the help of a DBMS.

The database management system is the link between the databases and the end users or application programs. It makes sure that the data is always organized and easy to find. Data is managed by the DBMS, is accessible, locked, and modifiable by the database engine, and has a logical structure defined by the database schema.

These three fundamental components provide concurrency, data security, integrity, and standard data management practices. The DBMS covers common database administration functions like change management. Most database management systems are responsible for automated rollbacks and restarts, besides the recording and auditing of actions occurring inside databases and the applications that can access them.

See More: What Is Cloud Migration? Definition, Process, Benefits, and Trends

The DBMS offers a centralized view of the data that may be accessed in a regulated manner by several users from numerous places. A DBMS, which offers multiple views of a single database structure, can restrict both what data and how the end user can view the data. The DBMS manages all requests, so end users and software programs are free from comprehending where the data is physically housed or what kind of storage medium it lives in.

DBMS can provide independent access to logical and physical data. As a result, users and programs don’t need to know where data is maintained or worry about adjustments to the data’s physical structure. Additionally, this means that it can protect data from being altered. Suppose the applications use an application programming interface (API) for the database that the management system provides; the developers don’t need to make changes to the programs simply because of changes made to the database.

Users of DBMS can design their databases per their needs. The term “DBMS” refers to the application programs and the database used. It offers a connection point between the data and the software program and enables database security. Additionally, it preserves data consistency when there are many users.

See More: What Is Private Cloud Storage? Definition, Types, Examples, and Best Practices

Types of Data Models in DBMS

A typical database management system can support the following types of data models:

1. Hierarchical model

In a database organized in a hierarchical structure, the data is collected in a tree-like form. This model represents some of the links in the actual world, such as recipes for food, sitemaps for websites, etc. A hierarchical model has the following characteristics:

  • One-to-many relationship: The one-to-many relationship between the datatypes is present in the data organization, which resembles a tree.
  • Parent-child relationship: Although a parent node might have more than one child node, every child node has a parent node.
  • Deletion problem: When a parent node is erased, all child nodes follow suit.
  • Pointers: Pointers navigate between the stored data and connect the parent and child nodes.

2. Relational model

One of the most frequently used data models is the relational model. The data in this model is kept as a two-dimensional table. The data storage takes the shape of rows and columns. Tables are a relational model’s fundamental building block. In the relational paradigm, the tables are also referred to as relations. The key traits of the relational model are as follows:

  • Tuples: The table’s rows are referred to as tuples. All the information about any object instance is contained in a row.
  • Attribute or field: The property that defines a table or relation is called an attribute. The attribute’s values ought to come from the same domain.

3. Object-oriented model

According to this paradigm, a database is a group of objects, or reusable software components, with related features and procedures. Various types of object-oriented databases exist, including:

  • Images and other media types that one cannot keep in a relational database are included in multimedia databases. 
  • Any object can link to any other object using a hypertext database. 

Although it helps organize vast volumes of different data, it’s not the best option for numerical analysis. The object-oriented database model, the most popular post-relational database model, incorporates tables without being restricted to them. These designs are also referred to as hybrid database designs.

4. Network model

The network model expands on the hierarchical model by enabling many-to-many relationships among linked records, which implies multiple parent records. The model is built using sets of related records on the basis of mathematical set theory. A network model has to offer the following features:

  • Increased capacity for relationship fusion: The more relationships in this model, the more connected the data is.
  • Several paths: There may be more than one path to the same record due to the increased number of relationships. This facilitates quick and easy data access.
  • Linked circular list: The circular linked list performs operations on the network model.

5. Float data model

One two-dimensional array of data elements makes up the float data model. It is a straightforward model where the database is shown as a table with rows and columns. The computer must read the complete table to access any data. The modes are hence sluggish and ineffective.

As the computer has to read the whole flat file into memory to access or change the data, this method is efficient only for minor data sets. A large amount of data cannot be stored using this model, and one must search the entire table to access any data, making it ineffective.

6. Entity-relationship (ER) Model

Relationships are established in this database model by classifying the object of interest as an entity, and its properties as attributes and these relationships are used to connect various entities. ER models are designed to illustrate relationships in a way that is understandable to multiple stakeholders.

This architecture is useful for creating databases that can later transform into relational model tables. To put it simply, an ER diagram is used to show the database’s logical structure simply. Since the ER model creates a conceptual understanding of the data, it can be used as a guide to building the database in the future.

7. Semi-structured data model

Because the semi-structured data model is a generalized version of the relational model that enables flexible data representation, it is impossible to distinguish between data and schema in this model because some entities may lack one or more attributes. In contrast, other entities may have additional characteristics that simplify updating the database’s schema.

Some entities in this model might lack certain qualities, while others might have an extra attribute. This approach allows for flexible data storage. Additionally, it gives the qualities of freedom. One can use it to describe interactions between databases that follow different schemas.

See More: What Is Cloud Encryption? Definition, Importance, Methods, and Best Practices

ACID Properties in DBMS

When modifications are made to a database, the data management should continue to be integrated. This is because if the data’s integrity is compromised, the entire data set will be disturbed and corrupted. A database’s contents can be accessed and modified as part of a single logical unit of work known as a transaction.

ACID Properties in DBMS

ACID Properties in DBMS

These transactions use read and write operations to access data. Specific characteristics, referred to as the ACID (Atomicity, Consistency, Isolation, and Durability) attributes, must be kept in mind in database transactions to ensure consistency. Let’s go over them in more depth.

1. Atomicity

Atomicity describes a situation in which the complete transaction either happens all at once or not at all. There is, therefore, no halfway point. There can never be a partial transaction. Every transaction can be considered a separate unit that either executes entirely or not at all. Here, we have these two operations:

  • Commit: The changes made are apparent to us if a transaction commits. Thus, the “All or nothing rule” is another name for atomicity.
  • Abort: If a transaction fails, we won’t be able to see any database modifications that were done.

2. Consistency

We must uphold the integrity requirements for any particular database to be consistent before and after a transaction. It is a reference to a database’s accuracy. In DBMS, data integrity must be maintained, necessitating that any database modifications always be saved. Data integrity is crucial in transactions because it ensures that the database is consistent before and after the transaction. The information should always be accurate.

3. Isolation

Multiple transactions can coincide with isolation in place, preventing inconsistent database states. An autonomous or uninvolved transaction takes place. Unless this specific change in the transaction has been committed or put into the memory, any changes that take place in that transaction will never be visible to the other transactions.

The virtue of isolation guarantees that when we run the transactions simultaneously, the produced state will be identical to the state that was run serially and in a particular order. Transactions take place without interruption and independently. Other transactions cannot see a transaction’s intermediate state.

4. Durability

Durability in DBMS refers to the guarantee that data remains in the database permanently once an operation has been completed. The data should be sufficiently robust to continue to work in the case of a system crash or failure. However, if it disappears, the recovery manager is now in charge of ensuring the database is durable.

We must use the COMMIT command to commit the settings every time we make changes. According to the durability feature, the database updates and modifications are written to and saved on the disk once a transaction’s execution is complete.

See More: What Is Community Cloud? Definition, Architecture, Examples, and Best Practices

DBMS Examples

You can use multiple database management systems or DBMS software for information storage, organization, and data analysis. Some of the top options include:

1. Microsoft Access

Microsoft’s DBMS, known as Access, combines a graphical user interface, software development tools, and the relational Microsoft Jet Database Engine. Due to the graphical user interface, it is simple. The professional and higher editions of Microsoft Office include it as part of their suite of programs.

The advantages of MS Access include its quick and easy creation of a fully effective relational database management system, ease of data import from many sources, and simplicity of customization to suit individual and business requirements. The drawback of MS Access is that it works well only for small and medium-sized enterprises.

2. MySQL

This is an open-source relational database management system (RDBMS) with a client-server structure (open source, in a nutshell, is software ready for alterations and free usage). 

Let’s briefly discuss what client-server is. Clients are the devices that install and use RDBMS software. They establish a connection to the RDBMS server whenever they require data access. The “client-server” component is simply that. Uncomplicated syntax and moderate feature complexity characterize MySQL. Developers even regard MySQL as a database with a human-like language. One drawback of MySQL is scalability, which is ingrained in its code, and was not considered when it was being developed.

3. Oracle Database

It is the company’s fourth attempt at developing a relational database management system. Large enterprises, especially, can store larger volumes of data on Oracle databases. Additionally, it is adaptable and helpful for shared structured query language (SQL) and locking. The system’s relational database framework allows users immediate access to data items.

It builds a powerful engine for synchronous data processing combined with the ability to process data in memory. It’s not a good idea to use Oracle databases right away. Having certified Oracle DB engineers run it is preferable. If you plan to use Oracle DB, you should think about hiring specialized professionals.

4. MongoDB

MongoDB is a general-purpose data store. Compared to other forms of databases, this offers several benefits. MongoDB data is mapped to a configurable schema. You can quickly modify how your data is kept if the requirements of your application change. This enables you to refine fresh ideas much more rapidly.

You can lock down your schema as much or as little as you’d want because MongoDB also offers schema validation. This indicates that it can manage any data structuring needs you may have now or in the future. MongoDB allows users to combine documents via references and procedures that cater to different needs, including $lookup.

See More: What Is Software as a Service (SaaS)? Definition, Examples, Types, and Trends

5. IBM Db2 DBMS

The world’s top database specialists created IBM Db2, which gives developers, DBAs, and enterprise architects the tools they need to execute real-time analytics and low-latency transactions for even the most demanding workloads. Db2 is the tried-and-true hybrid database that offers extreme availability, sophisticated integrated security, seamless scalability, and intelligent automation for systems that run the world from microservices to AI workloads.

Virtually all of your data is now accessible across hybrid cloud or multi-cloud settings to power your AI applications, thanks to the majority of the Db2 family being made available on the IBM Cloud Pak for Data platform, either as an add-on or an included data source service.

6. Amazon RDS

The managed SQL database service known as Amazon Relational Database Service (RDS) is offered by Amazon Web Services (AWS). Amazon RDS supports a variety of database engines for data storage and organization. Additionally, it supports activities related to relational database maintenance, including data migration, backup, recovery, and patching. Amazon RDS makes it easy to set up, run, and scale a relational database in the cloud.

While automating time-consuming administrative activities like hardware provisioning, database setup, patching, and backups, it offers affordable and expandable capacity. It gives you more time to concentrate on your applications, giving them the quick response, high availability, security, and compatibility required.

7. PostgreSQL

Open-source database management system PostgreSQL is designed for businesses. For flexibility and SQL compliance, it allows relational and non-relational queries in both SQL and JSON. PostgreSQL is an effective open source object-relational database system. It has been actively developed for more than 15 years, and because of its tried-and-true architecture, it enjoys a solid reputation for dependability, data integrity, and correctness.

All popular operating systems, including Linux, UNIX, and Windows, support PostgreSQL. Advanced data types and performance-enhancing tools are only supported by pricey commercial databases like Oracle and SQL Server; however, PostgreSQL has these features built-in. It also goes by the name Postgres.

8. Apache Cassandra

With no single point of failure and the ability to handle massive volumes of data across numerous commodity servers, Apache Cassandra is a distributed database that is highly scalable and highly functional. It belongs to the NoSQL database family. The capacity to manage organized, semi-structured and unstructured data is a strength of Apache Cassandra.

Initially created by Facebook, Cassandra was made available to the public in 2008 before becoming one of the top-level Apache projects in 2010. Major corporations particularly benefit from their capacity to handle enormous volumes. Because of this, many significant corporations, like Apple, Facebook, and Instagram, are currently using it.

See More: What Is Platform as a Service (PaaS)? Definition, Examples, Components, and Best Practices

Takeaway 

Despite already being an established technology, database management systems are constantly evolving. According to a 2021 report by Expert Market Research, the global DBMS market was worth more than $63 billion in 2020 and will grow at a rate of 12.4% between 2022 and 2027. By knowing the ins and outs of this technology, companies can invest in the best DBMS solutions and maximize the power of data available to their business. 

Did this article help you understand how database management systems work? Tell us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you! 

MORE ON CLOUD

Chiradeep BasuMallick
Chiradeep is a content marketing professional, a startup incubator, and a tech journalism specialist. He has over 11 years of experience in mainline advertising, marketing communications, corporate communications, and content marketing. He has worked with a number of global majors and Indian MNCs, and currently manages his content marketing startup based out of Kolkata, India. He writes extensively on areas such as IT, BFSI, healthcare, manufacturing, hospitality, and financial analysis & stock markets. He studied literature, has a degree in public relations and is an independent contributor for several leading publications.
Take me to Community
Do you still have questions? Head over to the Spiceworks Community to find answers.