7 Types of DBMS with Examples
Explore the different types of DBMS including relational, object-oriented, hierarchical, network, NoSQL, column-family, document-oriented, graph, time-series and in-memory. Learn about their unique features, use cases, and examples of popular software for each type of DBMS.
What is DBMS?
A DBMS (Database Management System) is a software application that interacts with end-users, other applications, and the database itself to capture and analyze the data. A DBMS is a collection of programs that enables you to store, modify, and extract information from a database. It provides an interface for users to interact with the database and perform various operations like creating, reading, updating, and deleting data.
The DBMS is responsible for managing the database and ensuring that data is stored and retrieved efficiently, accurately, and securely. It also provides tools for data backup and recovery, data integrity, and data security. A DBMS can include additional functionality such as data validation, data access control, and reporting.
There are many different types of DBMS, each with its own strengths and weaknesses, and each suitable for different types of applications and environments. Examples include relational DBMS, object-oriented DBMS, NoSQL DBMS, and graph DBMS.
Types of DBMS
- Relational DBMS (RDBMS)
- Object-oriented DBMS (OODBMS)
- Hierarchical DBMS (HDBMS)
- Network DBMS (NDBMS)
- NoSQL DBMS
- Document-oriented DBMS
- Column-family DBMS
- Key-value databases
- Graph DBMS
- In-memory DBMS
- Time-series DBMS
This table shows the types of DBMS
|Type of DBMS||Description||Examples|
|Relational DBMS (RDBMS)||A type of DBMS that organizes data into tables (also known as relations) and allows for the creation of relationships between them.||MySQL, Oracle, and Microsoft SQL Server|
|Object-oriented DBMS (OODBMS)||A type of DBMS that organizes data into objects and allows for the creation of classes and inheritance.||MongoDB and Apache Cassandra|
|Hierarchical DBMS (HDBMS)||A type of DBMS that organizes data into a tree-like structure, with each record having a single parent and zero or more children.||IBM’s Information Management System (IMS)|
|Network DBMS (NDBMS)||A type of DBMS that organizes data into a network-like structure, with multiple parents and children for each record.||Integrated Data Store (IDS)|
|NoSQL DBMS||A type of DBMS that does not use the traditional table-based structure of RDBMS. Instead, it uses a more flexible, document-based or key-value-based data model.||MongoDB, Cassandra, Redis and Couchbase|
|Document-oriented DBMS||A type of NoSQL DBMS that organizes data into documents, similar to JSON or XML, and is often used for unstructured or semi-structured data.||MongoDB and Couchbase|
|Column-family DBMS||A type of NoSQL DBMS that organizes data into columns rather than rows, and is optimized for handling large amounts of data.||Apache Hbase and Google BigTable|
|Key-value databases||A type of NoSQL DBMS that store data as a mapping of keys to values and are optimized for high-speed data retrieval.||Redis and Riak|
|Graph DBMS||A type of NoSQL DBMS that organizes data in the form of nodes and edges, and is optimized for handling relationships between data.||Neo4j and Amazon Neptune|
|In-memory DBMS||A type of DBMS that stores all data in the memory (RAM) rather than in disk storage. This allows for faster data access and is commonly used for real-time data processing or high-performance computing.||SAP HANA and Apache Ignite|
|Time-series DBMS||A type of DBMS that is optimized for handling time-series data, which is data collected at regular intervals over time.||InfluxDB and TimescaleDB|
Relational DBMS (RDBMS)
A Relational Database Management System (RDBMS) is a type of DBMS that organizes data into tables (also known as relations) and allows for the creation of relationships between them. The data in an RDBMS is stored in a structured format, with each table consisting of rows and columns. Each row represents a single record, and each column represents a field of data within that record.
Features of RDBMS
One of the key features of an RDBMS is the use of a primary key and foreign keys to establish relationships between tables. A primary key is a unique identifier for each record in a table, and a foreign key is a reference to the primary key of another table. This allows for the creation of relationships between tables, such as one-to-one, one-to-many, and many-to-many.
Examples of RDBMS
RDBMSs are widely used in many different industries, from small businesses to large enterprise systems. Some popular examples of RDBMSs include MySQL, Oracle, and Microsoft SQL Server. They are well suited for structured data and support SQL (Structured Query Language) which is a standard language for interacting with relational databases.
RDBMSs provides a lot of functionality like ACID properties (Atomicity, Consistency, Isolation, Durability), Indexing, backup, recovery, security and many more.
Object-oriented DBMS (OODBMS)
An Object-oriented Database Management System (OODBMS) is a type of DBMS that organizes data into objects and allows for the creation of classes and inheritance. In an OODBMS, data is stored in a format that is similar to objects in object-oriented programming languages, such as Java or C++. Each object has its own properties, methods, and behaviors, and can be part of a class or hierarchy of classes.
Features of OODBMS
One of the key features of an OODBMS is the ability to model complex relationships and hierarchies within the data. For example, an OODBMS can model an object such as a car, which has properties such as make, model, and year, and methods such as start and stop. It can also model relationships between objects, such as a car having a one-to-many relationship with its parts.
Examples of OODBMS
OODBMSs are well suited for handling complex, unstructured, or semi-structured data, and are often used in applications such as engineering, geographic information systems, and multimedia. Some popular examples of OODBMSs include MongoDB, Apache Cassandra, and ObjectDB. They are not as popular as RDBMSs but still have their use cases.
However, OODBMSs are not as standardized as RDBMSs, and the query languages and interfaces used to interact with them can vary widely between different systems. Additionally, OODBMSs generally have less support for ad-hoc queries, reporting, and analysis than RDBMSs.
Hierarchical DBMS (HDBMS)
A Hierarchical Database Management System (HDBMS) is a type of DBMS that organizes data in a hierarchical tree-like structure. In an HDBMS, data is represented as a series of records, with each record having one parent record and one or more child records. This creates a parent-child relationship between records, with the parent record being at the top of the hierarchy and child records being at the bottom.
Features of HDBMS
The hierarchical model is closely related to the tree data structure, with the parent-child relationship being similar to the parent-child relationship between nodes in a tree. The data is stored in a tree-like structure, with each node representing a record and each branch representing a relationship between records. This structure allows for easy traversal of data, but can make it more difficult to represent more complex relationships between records.
Examples of HDBMS
HDBMSs were popular in the past, especially for applications such as data modeling for manufacturing systems and other similar applications. IBM’s Information Management System (IMS) is an example of a HDBMS. However, they have largely been replaced by more modern DBMSs, such as RDBMSs and OODBMSs, which provide more flexibility and scalability.
HDBMSs are not as popular as RDBMSs or OODBMSs and are not used as much as they used to be. They are not suitable for large and complex data sets, and are not very good at handling many-to-many relationships between records. They are best suited for highly structured data and applications that require a high level of data integrity and security.
Network DBMS (NDBMS)
A Network Database Management System (NDBMS) is a type of DBMS that organizes data in a network structure. In a NDBMS, data is represented as a series of records, with each record having multiple parents and children. This creates a many-to-many relationship between records, with records being connected to multiple other records, creating a web-like structure.
The network model is closely related to the graph data structure, with the many-to-many relationship being similar to the edges between nodes in a graph. The data is stored in a web-like structure, with each node representing a record and each edge representing a relationship between records. This structure allows for easy traversal of data, and can represent complex relationships between records.
NDBMSs were popular in the past and were used for applications such as data modeling for manufacturing systems, inventory management, and other similar applications. Integrated Data Store (IDS) and Integrated Data Store II (IDS II) developed by Integrated Data Systems (IDS) are examples of NDBMS. However, they have largely been replaced by more modern DBMSs, such as RDBMSs and OODBMSs, which provide more flexibility and scalability.
NDBMSs are not as popular as RDBMSs or OODBMSs and are not used as much as they used to be. They are best suited for applications that require a high level of data integrity and security, and are not suitable for large and complex data sets.
NoSQL (Not Only SQL) Database Management Systems (DBMS) refer to a type of DBMS that do not use the traditional relational model and SQL (Structured Query Language) for data storage and retrieval. Instead, they use alternative data models and query languages that are more suitable for handling large and unstructured data sets, as well as for distributed and cloud-based environments.
NoSQL DBMSs are designed to handle big data and high-scale data processing workloads, they can handle large volume, high velocity, and varied data. They are often used in web, mobile, gaming, and social media applications, as well as in the Internet of Things (IoT), real-time analytics, and machine learning applications.
There are several different types of NoSQL databases. Some popular types of NoSQL databases include:
Document databases, such as MongoDB and Couchbase, which store data in the form of documents and allow for rich, nested data structures.
Column-family databases, such as Apache Cassandra and Hbase, which store data in the form of columns rather than rows and are optimized for high read and write performance.
Key-value databases, such as Redis and Riak, which store data as a mapping of keys to values and are optimized for high-speed data retrieval.
Graph databases, such as Neo4j and OrientDB, which store data as nodes and edges in a graph and are optimized for traversing complex relationships.
NoSQL databases are more flexible and can handle unstructured data, which can be a great solution for big data and real-time data-processing use cases. However, they lack the transactional guarantees and consistency provided by RDBMSs and are not always suitable for complex queries, joins, and transactions.
An In-memory Database Management System (DBMS) is a type of DBMS that stores all of its data in the memory, as opposed to storing it on disk. This allows for faster data access and processing, since data does not need to be read from disk and can be accessed directly from memory.
In-memory DBMSs are designed to handle real-time, high-concurrency, and high-throughput workloads. They are often used in applications such as online transaction processing (OLTP), real-time analytics, and high-frequency trading, where low latency and high performance are critical.
In-memory DBMS Benefits
In-memory databases use the same data models and query languages as traditional relational databases, and can be used as a drop-in replacement for traditional databases in many cases. They can also be used in conjunction with traditional databases, with the in-memory database being used as a cache or buffer for frequently accessed data.
In-memory DBMS Limitations
In-memory databases can be very fast, but they have some limitations. The biggest limitation is their cost, as they require large amounts of memory to store data, and memory is generally more expensive than disk storage. Additionally, the amount of data that can be stored in memory is typically smaller than the amount that can be stored on disk, so it may not be suitable for very large data sets.
Examples of In-memory DBMS
In-memory DBMSs are becoming more popular as the cost of memory has decreased and the need for real-time data processing has increased. Some examples of in-memory databases are SAP HANA, Oracle TimesTen, and IBM DB2 BLU.
A Time-Series Database Management System (DBMS) is a type of database that is specifically designed to handle time-series data, which is a sequence of data points collected at regular intervals of time. This data typically includes measurements such as temperature, pressure, humidity, stock prices, and so on, and it is usually collected in real-time from various sources, such as sensors, IoT devices, and financial systems.
Features of Time-Series DBMS
Time-series DBMSs are designed to handle large amounts of data, high write and read rates, and complex queries on time-based data. They typically provide features such as data compression, data retention policies, and automatic downsampling, which are optimized for time-series data.
Examples of Time-Series DBMS
Time-series DBMSs are used in a wide range of applications, including monitoring and control systems, industrial automation, financial analysis, and scientific research. Some examples of time-series databases are InfluxDB, OpenTSDB, TimescaleDB and Prometheus.
They are often used in conjunction with other types of databases, like relational or NoSQL, to store the metadata related to the time-series data, making it easier to search, query, and analyze the data.
Time-series databases can handle large amounts of data and support very fast queries and are optimized for time-series data. However, they typically lack some of the features and functionality of traditional relational databases, such as support for complex transactions and joins.
- What is DBMS? (Components, Features & Advantages)
- Types of Data Models in DBMS
- Hashing in DBMS
- Deadlock in DBMS
- Functional Dependency in DBMS
- Referential Integrity in DBMS
- Difference between DBMS and RDBMS
- ACID Properties DBMS (Atomicity, Consistency, Isolation, Durability)