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A database is a collection of interrelated data, such as user data, company data, university data, etc. These collections can store data in a structured format, which allows developers to access and update data easily. Databases are important for storing large amounts of data and retrieving them whenever needed. A system created to manage these data efficiently is called a database management system.
In this DBMS tutorial, you will learn what a database management system is, the key features, characteristics, components, examples, pros, cons, etc. of DBMS. You will also learn different database languages and some advanced concepts like normalisation in DBMS, ACID properties of DBMS, etc.
A database management system is a set of tools, software, and programming languages that help to create, control, and manage databases. It is a middleware between the user and data, allowing for easy access, manipulation, and retrieval of information.
Mainly, DBMS is used to insert data in the database, and easily retrieve and update the data. Moreover, DBMS also provides various tools for data administration. These tools also allow control of the access to data. For example, you can give limited access to data to your employee based on their role rather than giving access to the whole database.
The commonly used DBMS software are MySQL, SQLite, PostgreSQL, Microsoft Access, etc.
The concept of database management has evolved significantly over the decades. Initially, in the early 1950s, the data was stored in files and managed by the operating system. For example, to create a database for a university, it was required to create different files for administration tasks, store student and teacher data, etc. This method was prone to data duplication and complex to manage large databases. So, there was a need for a structured database.
The modern era of databases began in the 1960s with the advent of computer technology. In 1960, Charles W. Batchman and his team developed the first database management system called Integrated Data Store (IDS). This IDS is a hierarchical DBMS that stores the data in a tree-like structure. It has a parent-child relationship between multiple data records.
The significant breakthrough came in 1970 when Edgar F. Codd, an IBM researcher, introduced the relational model. This model revolutionised database management by focusing on data retrieval based on content rather than following navigational paths.
Now, let’s look at the evolution of the database management system every decade starting from 1960.
Year | Event |
1960s | Introduction of hierarchical (IMS by IBM) and network (CODASYL) database models. |
1970 | Edgar F. Codd introduces the relational model, which uses tables to organise data, making data management more flexible and efficient. |
1979 | Oracle has released the first commercial relational database. |
1980s | Development of object-oriented databases, integrating object-oriented programming features with database capabilities. |
1990s | Emergence of object-relational databases, combining relational and object-oriented features. |
2000s | The rise of NoSQL databases like MongoDB and Cassandra to handle large volumes of unstructured data is driven by the needs of web-scale applications. |
2010s | Introduction of cloud-based and distributed databases, such as Amazon DynamoDB and Google Spanner, offering scalability and global reach. |
Present | It has continued launching new versions of databases and focusing on integration with artificial intelligence, machine learning, and real-time data processing. |
A Database Management System (DBMS) streamlines the process of managing and accessing large volumes of data. These features ensure that data is organised, secure, and easily retrievable. Here are some of the key features:
These features collectively make DBMS an indispensable tool for managing large and complex datasets, ensuring data is accurate, secure, and readily available.
A Database Management System (DBMS) contains various components to manage and control the data. Here, we have covered some key components of the DBMS.
Here, we have covered some of the key characteristics of DBMS:
These days, all businesses, from small to large, are required to store data in a database. The DBMS makes it easy to manage and access the data. Here are some real-time use cases of database management systems.
There are multiple DBMS available in the market. Each serves a different purpose, and you can choose based on your organisation’s requirements. Here, we have covered some of the most popular DBMSs with an in-depth overview.
MYSQL is an open-source relational database management system (RDBMS) that stores structured data in table format. It is written in the C and C++ programming languages.
MySQL offers high performance and reliability and supports the SQL query language. It is mainly used for small—to medium-sized applications to store structured data. It is used by Facebook, Twitter, and some famous E-commerce websites.
PostgreSQL is also an open-source object-relational DBMS known for its strong standards of compliance and extensibility. It is mainly used with applications requiring complex queries, such as financial systems.
The main features of PostgreSQL are that it provides support for JSON and XML data types, robust concurrency control, and custom functions.
Oracle Database is a powerful enterprise-grade relational database management system known for its robust performance, scalability, and extensive feature set. It is mainly used for enterprise-level applications.
Oracle also provides the support for SQL. It also offers high security.
Microsoft SQL Server is a Relational Database Management System (RDBMS) developed by Microsoft in 1989. It is widely used with Microsoft products and is known for easy integrations with them. The main benefit of using the Microsoft SQL Server RDBMS is that it is available in multiple languages, such as English, Spanish, German, etc.
The main key features of Microsoft SQL Server are that it provides support for T-SQL, advanced analytics, in-memory processing, and robust security features.
MongoDB is a non-relational database developed in 2009. It stores data in JSON-like documents containing multiple objects. For each schema, you can create a single object to store the data in the MongoDB database. It is widely used to store unstructured data or with applications that require changing the database structure frequently.
Using the MongoDB database, you can easily perform horizontal scaling, as it allows you to add new attributes in JSON schema.
Amazon DynamoDB is a NoSQL database service. It is fully managed by Amazon Web Services(AWS). This web service runs in the cloud and facilitates database management. It is well-known for its high availability and scalability of databases.
Amazon DynamoDB is mainly used with applications that require real-time data processing. Amazon fully manages it, so users don’t need to worry about database hosting, managing security, and taking the database backup.
Also read: Difference Between DBMS and RDBMS
The main advantage of using a DBMS is that it allows you to store data in a structured manner and easily access it using queries. However, there are some more advantages of using a DBMS.
While DBMS offers various advantages, it also comes with drawbacks. Here, we have covered some of the disadvantages of the DBMS.
The file system is a traditional database management system in which data is stored in the form of files. There were many issues with using the file system, which were overcome by introducing the DBMS. Here are some of the advantages of DBMS over file systems.
Data models in a Database Management System (DBMS) define how data is structured, stored, and accessed. A DBMS can support the following types of database models.
In a hierarchical database model, data are organised in a tree-like structure, where each record has a single parent element and can have multiple children. So, it represents the parent-child relationship.
The one-to-many relationship is also a hierarchical database model. This model is mainly used in file systems or classifications. It is simple and efficient for storing hierarchical data, but it can be complex to access and change data.
The network model is an expanded version of the hierarchical model. It stores the data in the graph format, where the node represents the data record and the edges represent the relationship between the data records.
The network model is ideal for situations where we need to imply many-to-many relationships between data. In this model, a single parent can have multiple children, and a single child can have multiple parents. Also, there can be several paths to reach a particular record.
The relational model is one of the most used database models. It stores data in a table in a row and column format. Here, rows are called tuples, and columns are called attributes. Multiple tables can be linked using primary keys and foreign keys.
Oracle, MySQL, Microsoft SQL Server, etc., work based on the relational database model (DBMS). This DBMS provides the flexibility to use query languages like SQL to process the data.
The No-SQL data model stores the data in the key-value pairs and unstructured format. Mainly, it uses the JSON or XML format to store the data. It is ideal where horizontal scaling of the database is required.
DBMS like MongoDB, DynamoDB, CouchDB, etc. use the NO-SQL data model. This kind of data model is useful for content management systems and e-commerce websites, where you need to create dynamic schemas.
The object-oriented database model integrates the object-oriented programming concepts with the database technology. Here, data is represented as objects. It is suitable for applications requiring complex data representations, such as computer-aided design (CAD), multimedia applications, and software engineering.
DBMSs like ObjectDB and db4o use the object-oriented model. The main benefit of this model is that it supports complex data types. However, it has a steeper learning curve, which makes it complex to learn.
The entity-relationship model is used to define the logical structure of the database. It uses the ER diagram to develop the database structure. In the ER diagram, the main object is represented as an entity, and each entity can have multiple attributes of different types. Later, the ER diagram can be converted into a table format. Each table represents a single entity, and it has a number of columns equal to the number of attributes of the entity. Moreover, each table is connected using the foreign key, which represents the relationship between two entities.
The float data model uses a single two-dimensional array to store the data, where the single record is stored in a single row. To perform any operation like accessing, modifying, etc., the data is required to read the whole array, which is very inefficient. So, the float data model is not widely used with real-time applications.
Database languages are special kinds of programming languages that are used to create, manage, access, and manipulate databases. They are also used to create database structures, control access to the database, and handle database transactions. Here are the four primary database languages.
Data Definition Language (DDL) is used to define and manage database schema and structure. DDL commands help create, alter, and delete database objects such as tables, indexes, and views.
The data manipulation language is used to manipulate the database like selecting, inserting, updating, etc., data in the database table. Here are some common commands of SQL which are used to manipulate the database:
Data Control Language (DCL) is used to control access to data in the database. It includes commands to grant and revoke permissions to users and roles.
The transactional control language is used to manage transactions like commits and rollbacks to the database. Here are some common SQL commands that are used to manage transactions in the database.
Normalisation is a very important concept in this DBMS tutorial. It is a way of arranging data in a database so that it has no redundancy as well as dependency. It includes breaking up a database into two or more tables and establishing links between them to maintain data integrity and efficiency. The main purpose is to reduce data duplication and increase data reliability. Here are two objectives of normalisation in DBMS.
Normalisation of the database can be done using the normal forms. Here, we have covered the first three normal forms.
The table contains only atomic values if it is in its first normal form. It means the table can’t contain attributes having multiple values. If it does, you need to separate the record into two different records.
For example, consider the below table. It contains the student_phone multi-valued attribute.
Student_ID | Name | Student_phone | 1 | Alex | +91 93312321,
2 | John | +91 34324244 |
Student_ID | Name | Student_phone |
1 | Alex | +91 93312321 |
1 | Alex | +91 45345345 |
2 | John | +91 34324244 |
To convert the table in the 2NF, it should be in 1NF. Furthermore, all non-key attributes should be fully functional and dependent on the primary key. To achieve 2NF, you can create multiple tables from a single table.
For example, in the below table, each teacher can teach multiple subjects.
Teacher_ID | Subject | Teacher_phone |
1 | English | +91 93312321 |
2 | Computer science | +91 45345345 |
1 | Physics | +91 93312321 |
Here, we have converted the above table into 2NF.
Teacher_ID | Subject |
1 | English |
2 | Computer science |
1 | Physics |
Teacher_ID | Teacher_phone |
1 | +91 93312321 |
2 | +91 45345345 |
Here, a duplicate phone number is removed from the database.
A relation will be called in the third normal form if it is in the 2NF and doesn’t contain any transitive partial dependency.
Ensuring that no transitive dependency exists, meaning non-key attributes should not depend on other non-key attributes.
ACID stands for Atomicity, Consistency, Isolation, and Durability. These properties are essential for ensuring reliable processing of database transactions. Let’s understand each property one by one.
Atomicity ensures that each transaction is treated as a single unit. Either all transactions are completed, or none of them is. It also ensures that partial updates do not occur.
For example, in the banking system, if a transaction fails, the system ensures that neither money nor data is credited or debited but that the data remains in its original state.
Consistently ensure that a transaction brings the database from one valid state to another valid state, preserving the integrity constraints specified by the database after completing the transaction.
For example, in the baking system, the total amount of money should be constant even after making a transaction.
Isolation ensures that each transaction occurs independently without affecting the other.
For example, if person A transfers money to person B and person B transfers the money to person C concurrently, each transaction occurs independently, preventing data inconsistency.
Durability ensures that after completing the transactions, the final data is stored in the database permanently. This guarantees that the results of the transaction are never lost.
For example, after a successful money transfer, an updated balance is stored in the database, and it can’t be undone due to events like system or power failure.
The DBMS is continuously evolving by integrating AI, ML, blockchain, etc. with it. Several key trends and developments are expected to drive the future of DBMS, making them more robust, flexible, and efficient.
Learning DBMS is very important as each application uses the database. So, it is always important to understand the system that helps in managing and manipulating the data.
DBMS is fundamental for managing and organising large volumes of data efficiently. It ensures data integrity, security, and availability, which are critical for any organisation. Learning DBMS equips you with the skills to design, implement, and manage databases that support business operations and decision-making processes.
Furthermore, learning DBMS is also a strategic investment in your career, as job openings for database management-related roles are continuously increasing.
The DBMS opens up the door for a wide range of career opportunities. Here are some high-paying job roles, and you can choose any of them as your career.
Understanding and mastering Database Management Systems (DBMS) is essential for anyone involved in managing data. DBMS simplifies data management by providing structured storage, ensuring data integrity, and offering secure access and retrieval methods. It is a critical tool in various industries, from banking and social media to healthcare and finance, where data plays a vital role in operations and decision-making. As we’ve discussed in this DBMS tutorial, learning about DBMS equips you with valuable skills that are highly sought after in the job market, providing numerous career opportunities such as Database Administrator, Data Architect, and Data Engineer.
The future of DBMS looks promising with advancements in cloud computing, AI integration, and distributed databases. These technologies will continue to evolve, offering more robust, scalable, and efficient ways to manage data. Whether you are a student, a professional looking to enhance your skills, or someone interested in data management, learning DBMS is a strategic investment. For this, you can pursue the Accelerator Program in Business Analytics and Data Science at Hero Vired.
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