What is Database Management System: Components, Types, Benefits

Data is the top-most priority, and databases are the most common way for any data-driven organization to store data. With the explosion of web and cloud technologies everywhere, databases have evolved from traditional relational databases to advanced databases such as No-SQL and NewSQL. Each type has its use case and can handle structured, semi-structured, and unstructured data. 

However, managing a database can be difficult, especially when handling mission-critical tasks such as managing complex data, driving insights, and complying with laws and regulations. This is where Database Management Systems (DBMS) come into play. 

In this article, we will discuss DBMS in detail, and you will learn about its components, types, benefits, and more. 

What is a Database Management System?

To break down this term word by word, a database management system is a software or application used to manage a database. It acts as a link between the database and the end-user by making sure the data is always organized and easy to access. A DBMS allows you to perform many actions to manage database structure and manipulate the data. It enables altering the data format, field names, data types, and schema structure and lets you validate data according to your specific requirements. 

A database management system can be classified into various components and data models. Let's understand both elements in detail:

Components of DBMS

The main function of DBMS can be broken down into its different components. A DBMS has many integrated components, making it a complete software for delivering a consistent environment for creating, accessing, and modifying data. These components include:

• Storage Engine
• Metadata Catalog
• Database Access Language
• Query Processor

Let's see them in detail.

1. Storage Engine

A storage engine is a basic element of DBMS. It works alongside the file system at an OS level to store data. All the SQL queries that work on underlying data go through the storage engine.‍

2. Metadata Catalog

When data gets created, a DBMS records data with some metadata using the metadata catalog component. You can think of it as a centralized catalog of all data within a database. ‍

3. Database Access Language

Using database access language, you can interact with a database to perform any task, from creating databases to inserting or retrieving data. A basic DBMS supports one or multiple query languages, such as SQL. ‍

4. Query Processor

A query processor interprets your queries into an actionable command that a database can understand to perform the intended tasks.  

Data Models in DBMS

Data modeling defines the logical design and structure of a DBMS. It accommodates how data is stored, accessed, and updated. Here are some of the data models you can choose to define your database according to your requirements:

1. Hierarchical Model:

This model organizes and stores data into a tree-like structure. The hierarchy starts from single root data and expands, adding child nodes. Every child node has a single parent. This model describes many real-world examples, like an index of books and file systems on the computer. A widely known DBMS based on this data model is IBM Information Management System (IMS). ‍

2. Network Model:

A network model is the extension of the hierarchical model. Here, data is organized more like a graph and can have more than one parent. This model allows you to reach any node from the root node in many possible ways to access the data efficiently. However, the insertion and deletion process can take a lot of work. Examples of network model DBMS are an Integrated Database Management System (IDMS), an Integrated Data Store (IDS).

3. Relational Model:

This is the most widely used data model for DBMS. It stores data as rows and columns within a two-dimensional table. Each row contains an individual record, and each column represents an attribute. Major DBMS use this data model, including MySQL and PostgreSQL. ‍

4. NoSQL Model:

The NoSQL data model is an unstructured way of storing document data. The documents in this model look like a JSON string or key value-based object representation. A popular example of a DBMS that follows this model is MongoDB. 

3 Types of Database Management Systems

There are many different types of database management systems. Three of the popular ones are mentioned below: 

1. Relational Database Management Systems (RDBMS)

RDMS, also called SQL DBMS, is one of the most widely used DBMSs. It stores data in a structured manner and presents data as tables within rows and columns with a fixed schema, making it easy to understand how different data structures relate. In RDBMS, each row in the table holds a record with a key or a unique ID, and columns of the table include data attributes where each record has a value for each attribute. 

Let's take a quick example to understand how RDBMS works: Say you have two tables, a Customer table, and an Order table. 

A customer table contains data such as customer ID (key), customer name, address, and phone number. Here, the customer ID is the key that uniquely identifies a customer. 

However, the order table contains transactional data, including order ID (key), customer ID (foreign key), order data, etc. In this case, we have two unique IDs: an order ID to identify a specific order, and a customer ID or foreign key to link a customer from an order. 

Both of the tables become related based on shared customer ID. Therefore, you can query both tables for specific business purposes, such as formal reports and visualization, or simply use the data for other applications. 

Examples of some of the widely known RDBMS include MariaDB, Microsoft SQL Server, Oracle, Postgres, and MySQL.

2. Non-Relational Database Management Systems (NoSQL)

Non-relational or NoSQL DBMS are non-tabular and store data differently than relational tables. These databases can handle large amounts of unstructured or semi-structured data that evolve. There are broadly four types of NoSQL database systems: graph databases, document databases, key-value stores, and wide-column stores. Each type uses a different data model to organize data:

• Graph Database: These databases follow a network data model and store data as nodes rather than tables. Every node has a relationship with other nodes, and its schema evolves over time. These are useful for applications and services that map relationships, such as reservation systems, social media platforms, or customer relationship management. Neo4j and GraphDB are two examples of graph databases. 
• ‍Document Database: As the name suggests, this database stores semi-structured data in document format, usually JSON. Document databases are useful for flexible schema requirements and efficiently processing data at large scales, such as mobile applications and content management systems. A popular example of this database is MongoDB. 
• ‍Key Value Stores: It is based on a simple data model that pairs an attribute name or key with an associated value. Due to its simplicity, key-value stores are highly scalable and used to develop performant services such as caching in web applications and managing sessions of online website visitors. Major databases in this database include Redis and Riak. 
• ‍Wide Column Stores: Also known as a columnar data store, a wide column store is a NoSQL database in which names and formats of columns vary across rows, even within the same table. Unlike the traditional row-oriented data storage approach, it stores data in a column-oriented format. Cassandra and HBase are examples of wide-column stores. 

3. NewSQL Database Management Systems

NewSQL is a modern relational database system that aims to bridge the gap between SQL and NoSQL databases. As you know, NoSQL databases are known for scalability, while SQL databases are known for consistency. NewSQL gets the features of both databases in one place. It combines ACID compliance of relational database for consistency with horizontal scaling of NoSQL for scalability. Some of the NewSQL databases that are gaining popularity are VoltDB, CockroachDB, and Altibase. 

Benefits of Database Management Systems

DBMS offers a lot of benefits for managing data efficiently. Some of them are mentioned below:

1. Decrease Data Redundancy

Data redundancy leads to potential errors and needless storage consumption. To prevent this, DBMS centralizes data storage by establishing a single, authoritative source for all data. This centralized approach minimizes redundancy by ensuring that data is stored only once in the system without duplication. 

2. Improved Data Accessibility 

A DBMS comes with user-friendly interfaces and query languages to make data more accessible to retrieve and analyze. Its features, like data abstraction, filtration, manipulation, view creation, and secure remote access, allow you to access data from anywhere you want according to your specific requirements. 

3. Robust Data Security & Compliance

A DBMS enables you to take many security measures, such as multi-level access control, audit trails, and encryption, to secure and protect your data from unauthorized access. In addition to its robust security, DBMS also allows you to ensure HIPAA compliance and comply with other laws and regulations, such as GDPR, enabling operations at a multi-national level while safeguarding sensitive healthcare information.

4. Backup And Recovery 

DBMS offers automated procedures such as transaction log backups or point-in-time recovery for backup and recovery. You can customize these features according to your requirements to run regularly or at a specific time. This ensures a recent copy of the database is always available in case of data corruption, loss, or system failure. 

5. Complement DBMS With Airbyte 

By offering a streamlined method of integrating data from disparate sources into a central database system, Airbyte helps in leveraging DBMS capabilities. Its automated data pipeline serves as a medium that guarantees the accurate and consistent import of data into DBMS for analysis and storage. 

With features like over 350+ pre-built connectors, data transformation, monitoring, and scheduling capabilities, you can streamline data migration from any source to the DBMS of your choice. If you still don't find the connector to automate data replication with your DBMS, you can use its Connector Development Kit to build a custom one within minutes. Sign up with Airbyte Cloud and streamline data migration between any system to your DBMS today. 

Conclusion

Whether you have a small organization or a large enterprise, a DBMS can be beneficial for you to optimize operational efficiency. By using the information mentioned in this article, including DBMS components, data models, types, and benefits, you can build an understanding to harness the full potential of your database management systems.

If you've enjoyed reading this article and want more insights, no problem! We've got you covered. Check out our insightful piece on Structured vs Unstructured Data.