Published on November 12th, 2024
Database Management System (DBMS) is an important part of software development, as it supports the efficient storage and management of data. Data defines today’s applications and the DB system’s choice can drive the application’s performance, scalability, and security. There are relational databases like MySQL and NoSQL choices like MongoDB, each of which has its advantages to use for a particular development need.
This blog explores the top 10 most used database systems, their features, and benefits. By knowing these database systems, you can make informed decisions on how to handle the data for different uses.
Top 10 Most-Used Database SystemsÂ
Here, at iTechnolabs, our content team has conducted extensive research and compiled this list based on usage popularity, online reviews, and customer adoption. Here are the top 10 widely used DBMSs to explore:
Oracle
The Oracle database was developed by the Oracle Corporation in 1977 by Lawrence Ellison. It was the pioneer of enterprise grid computing and data warehousing. It used distributed computing to distribute nodes of the computer from various geographical locations to achieve a common goal. This feature improves the database flexibility and cuts costs.
Oracle also introduced key features like SQL queries, allowing users to efficiently interact with and manage data across complex systems. Known for robust performance, scalability, and security, Oracle’s database remains a popular choice for businesses looking to handle large volumes of data reliably and securely.
Key Features of Oracle
- High scalability and performance for large data volumes.
- Advanced security features, including encryption and auditing.
- Robust data management with support for various data types.
- Real-time data processing and analytics capabilities.
- Strong backup and recovery options for data protection.
- Comprehensive support for SQL and PL/SQL programming languages.
It works on different operating systems, including IBM, macOS, Windows, and more. It was also expanded in the late 1990s when support for Linux was added, doubling its versatility. Given that you can manage large amounts of data efficiently and effectively across different types of platforms, Oracle databases are commonly used in business.
| Oracle Pros | Oracle Cons | Use Cases |
| Optimized for large-scale transactions | Expensive licensing and maintenance fees | Used by large organizations for ERP systems |
| Advanced security protocols and encryption | Installation and tuning can be complex | Suitable for banking and financial applications requiring high-security |
| Easily scales with data growth | Requires significant hardware resources | Ideal for large-scale data warehousing solutions |
| Extensive documentation and support options | Requires skilled personnel for effective use | Used for managing patient data and compliance |
| Supports advanced analytics and data warehousing | Fewer open-source integrations compared to competitors | Used for customer management and billing systems |
Also Read: Top 15 Best Web App Database List
MYSQL
MySQL (written in C and C++) is another leading database management system that software developers use and is used most of all by developers. MySQL is an open-source RDBMS (relational database management system) that offers advanced features.
It works mainly with the web development domain because it is stable, robust, and mature. If the survey is to be believed then, 50% of the developers use MySQL, and some of the popular organizations like Google, and Facebook, among other companies are also using MySQL. Not only that, it gives you ACID (Atomicity, Consistency, Isolation, and Durability), making it more reliable.
Key Features of MySQL
- Open-source and cost-effective with wide community support.
- High-speed read and write operations for fast performance.
- Cross-platform compatibility for various operating systems.
- Strong data security with user authentication and access controls.
- Support for large databases and concurrent user access.
- Flexible storage engine options for different data needs.
| MySQL Pros | MySQL Cons | Use cases |
| Available as a free, open-source option | Lacks some advanced features found in enterprise databases | Commonly used for dynamic websites and e-commerce platforms |
| User-friendly with a simpler setup | Not ideal for extremely large or complex databases | Supports CMSs like WordPress, Joomla, and Drupal |
| Compatible with multiple platforms and programming languages | Limited to handling large-scale transactions in complex applications | Used for managing product catalogs, customer info, and transactions |
| Large community offering extensive resources and support | Lacks some enterprise-level capabilities like native analytics | Suitable for small to medium data warehousing needs |
| Requires fewer resources, making it ideal for smaller projects | Requires additional tools for robust encryption | Often embedded in lightweight IoT applications |
Microsoft SQL Server
It was developed by Microsoft and launched in 1989. It is one of the most widely used RDBMS for on-premise and cloud environments. Microsoft SQL Server is written in C and C++. Being a multimodel database, it supports JSON, SQL, and Spatial Data. Intelligent by nature, it lets businesses scale their performance, availability, and security with their needs.
Key Features of Microsoft SQL
- Comprehensive data security with encryption and auditing tools.
- Advanced data analytics and integration with Microsoft tools.
- High availability and disaster recovery options for continuity.
- In-memory processing for enhanced performance.
- Support for both structured and semi-structured data.
- Scalable architecture suitable for enterprise environments.
It is used in IT environments for transaction processing, business intelligence, and analytics applications; and is great for building web apps.
| Microsoft SQL Pros | Microsoft SQL Cons | Use Cases |
| Optimized for high transaction volumes | Enterprise licensing can be costly | Widely used for large-scale enterprise applications and ERP systems |
| Advanced security protocols, data encryption, and auditing | Primarily runs on Windows, limiting cross-platform use | Ideal for financial services requiring high security and compliance |
| Seamlessly integrates with Microsoft ecosystem (Excel, Power BI, etc.) | Requires significant resources, especially for complex queries | Used in BI for reporting, data visualization, and analytics |
| Easily handles large data workloads | Setup, configuration, and maintenance can be complex | Suitable for healthcare systems managing large patient databases |
| Extensive documentation and support from Microsoft | Less compatible with open-source tools compared to other databases | Used in retail for inventory, customer data, and transaction management |
PostgreSQL
PostgreSQL is a database management system written in C that can manage a tremendous amount of data. It is actually an ORDBMS (Object Relational database Management System) and is free and open source. Businesses can leverage its robust features to achieve their data handling objectives. Its inheritance and function loading are used to enhance extensibility, and reliability, and to ensure data integrity offered.
Key Features of PostgreSQL
- Open-source with strong community support and regular updates.
- Advanced support for complex queries and data types.
- ACID compliance for reliable transaction handling.
- Extensible architecture with custom functions and plugins.
- High compatibility with multiple programming languages.
- Strong data integrity and security features.
PostgreSQL is used by big companies like Apple, Cisco, Skype, and IMDb. Some of its major applications include building gaming applications, database automation tools, and domain registrations.
| PostgreSQL Pros | PostgreSQL Cons | Use cases |
| Free to use, with extensive community support | Can be slower with high-volume, complex queries | Ideal for data analytics and warehousing in medium to large datasets |
| Maintains high reliability and data integrity through ACID compliance | Requires expertise in setup and optimization | Suitable for financial services requiring complex transactions and strict integrity |
| Supports custom functions, data types, and extensions like PostGIS | Primarily community-supported, with fewer enterprise-level support options | Used for GIS, mapping, and spatial data analysis |
| Works well across multiple OS (Windows, Linux, macOS) | May be slower for very large transaction-heavy applications | Widely used in SaaS, e-commerce, and content-heavy websites |
| Supports data encryption and access control | Certain features need manual configuration or extensions | Used for analytics in IoT, handling real-time data collection and processing |
MongoDB
It was created around 2009 by 10gen to address limitations in Relational Databases. It’s built as a NoSQL document oriented database, teamwork to its flexible schema designs, scalability, performance and especially to work efficiently with unstructured data. Modern applications that need constant data processing and scalability are better suited for use with MongoDB.
MongoDB is a cross-platform NoSQL database written in C, C++, and JavaScript. It is a high-speed database where the data is stored in some JSON-like document format. This database can be used as a database for mobile app development, analytics in real-time, IoT, etc. Using OOPS, MongoDB handles document data and doesn’t face problems loading and accessing the data into RDBMS. It is a scalable, flexible, and out-of-the-box relational database.
Key Features of MongoDBÂ
- Schema-less, flexible document model for dynamic data.
- High scalability with horizontal sharding across clusters.
- Support for real-time data processing and analytics.
- High availability with built-in replication and failover.
- Easy integration with modern programming languages.
- Indexing and aggregation capabilities for faster queries.
| MongoDB Pros | MongoDB Cons | Use Cases |
| Allows dynamic, schema-less structure ideal for unstructured data | Not suitable for multi-document ACID transactions | Commonly used for CMSs to store diverse content types and user data |
| Easily scales horizontally across servers, handling large data volumes | Requires significant memory for high-speed processing | Used for storing and managing product information, reviews, and orders |
| Developer-friendly with fast deployment | Not ideal for complex join queries or transactional systems | Ideal for analytics that require real-time data processing |
| Built-in geospatial features for location data | May result in data duplication without relational join | Useful for apps requiring geolocation, like delivery or ride-sharing platforms |
| Stores data in a JSON-like format, ideal for nested data structures | Fewer integrations with reporting and BI tools compared to SQL databases | Used for storing unstructured, large-scale sensor and IoT data |
Related: Firebase vs MongoDB
Redis
Redis is an open-source database management system that is ranked as the most favorite database platform. It is an in-memory key-value database that supports various data structures and has incredible speed like no other. Unlike other databases, Redis can process millions of requests, which makes it a suitable database to use in applications such as gaming, IoT, financial services, and so forth.
Key Features of Redis
- In-memory data storage for ultra-fast performance.
- Supports various data structures like strings, lists, and sets.
- Built-in replication and high availability for reliability.
- Persistence options for data durability.
- Pub/Sub messaging for real-time data communication.
- Simple and flexible caching capabilities.
Furthermore, it may also be used as a distributed cache and is a perfect choice for real-time analytics like social media, personalization, ad targeting, etc.
| Redis Pros | Redis Cons | Use Cases |
| Extremely fast read/write operations | Limited by available memory; not ideal for large datasets | Ideal for caching frequently accessed data to reduce the load on primary databases |
| Handles strings, hashes, lists, sets, and more | Limited query capabilities; lacks support for complex joins | Used for real-time data analytics, leaderboards, and session tracking |
| Offers snapshotting and AOF for data durability | Limited scalability for write-intensive workloads | Used for session storage in web applications due to low-latency access |
| Lightweight with easy deployment and low resource requirements | Basic security without strong encryption options; better suited for internal networks | Acts as a message broker in applications requiring real-time communication |
| Supports replication for high availability and clustering | Lacks full ACID compliance for transactions, limiting complex use cases | Used for live gaming leaderboards and counters due to fast, real-time updating |
SQLite
SQLite is a relatively lightweight and simple database management system (DBMS). It stores data in only one file on your device, making it simpler to set up and use. It’s often used to provide quick data access in mobile apps, web browsers, and small apps. It conforms to the standard SQL to control and query data, so users can easily add, update, and delete records. Developers love SQLite for its speed and ease of integration with both self-contained applications and projects that require a server. For developers looking for an efficient database solution, SQLite is a great choice.
Key Features of SQLiteÂ
- Lightweight and serverless, requiring minimal setup.
- Self-contained with a single file for easy distribution.
- ACID compliance for reliable transactions.
- Cross-platform compatibility across various operating systems.
- Fast performance for small to medium-sized applications.
- Supports a rich set of SQL features with limited overhead.
| SQLite Pros | SQLite Cons | Use Cases |
| Simple setup with no server required | Not ideal for high-concurrency environments | Commonly used in mobile apps for local data storage |
| Operates directly on disk files, eliminating the need for a separate server | Performance degrades with very large datasets (limited to 140 TB) | Ideal for embedded systems and devices with limited resources |
| Runs on various operating systems and platforms | Limited security features compared to server-based databases | Suitable for small applications that require lightweight data management |
| Ensures reliability and data integrity through ACID transactions | Missing advanced features like stored procedures and user-defined functions | Excellent for prototyping and development testing |
| Supports a subset of SQL for data manipulation and querying | Can be less efficient for complex queries and large joins | Often used in IoT devices for local data storage and processing |
ElasticSearch
Elasticsearch is a distributed, scalable, open-source search and analytics engine. It is built on top of the Apache Lucene, offering full-text search capability, and is very suitable for applications requiring fast, scalable retrieval of data. ElasticSearch supports a range of data types and has plenty of features like full-text search, faceted search, and geospatial queries just to name a few. The RESTful API and the JSON format make it convenient for applications to use.
Key Features of ElasticSearchÂ
- Full-text search capabilities with powerful query DSL.
- Real-time data indexing for immediate search results.
- Distributed architecture for horizontal scalability.
- Built-in data analytics and aggregation features.
- RESTful API for easy integration and access.
- High availability through automatic data replication and sharding.
ElastiSearch is often used in log and event data analysis as a core component of the ELK Stack (Elasticsearch, Logstash, Kibana) providing organizations the ability to handle large volumes of data efficiently. One popular example here is Netflix. It uses Elasticsearch to power its recommendation engine and enhance user search experiences, enabling fast, scalable data retrieval across massive content libraries to deliver personalized recommendations.
| ElasticSearch Pros | ElasticSearch Cons | Use Cases |
| Highly efficient for complex search queries and text analysis | Requires significant configuration for optimal performance | Used to build search engines for websites and applications |
| Easily scales horizontally to handle large volumes of data | Can consume a lot of RAM and CPU resources | Ideal for analyzing log files and event data in real time |
| Supports near real-time indexing and search capabilities | Not suited for applications requiring strict transaction consistency | Often paired with Kibana for visualizing large datasets |
| Uses a JSON-based document format, allowing for schema flexibility | Requires knowledge of its query DSL and architecture | Used to implement powerful search functionalities for e-commerce platforms |
| Supports advanced data aggregation for analytics | Requires regular maintenance for optimal performance | Used for monitoring infrastructure and application performance metrics |
IBM DB2
IBM DB2 is a highly effective software for data storage and data management for businesses. It’s flexible for many applications and supports structured and unstructured data. It is well known for its high performance, strong reliability, and security that protects sensitive data. DB2 provides easy-to-use tools to help people analyze and report their data to get valuable insights. Also, the DB2 supports cloud environments, so a business can grow its operations based on the need.
Key Features of IBM DB2
- Advanced data security with encryption and access controls.
- High performance for transactional and analytical workloads.
- Support for both structured and unstructured data types.
- Scalability options for large enterprise applications.
- Strong backup and recovery features for data protection.
- Integration with AI and machine learning tools for analytics.
Overall, IBM DB2 is a trusted choice when organizations need to handle large quantities of data with high efficiency and high security.
| IBM DB2 Pros | IBM DB2 Cons | Use Cases |
| Optimized for large workloads and high transaction volumes | Licensing and maintenance can be expensive | Commonly used for ERP systems in large organizations |
| Advanced security features, including encryption and access controls | May take time for users to become proficient | Suitable for banking applications requiring robust security and compliance |
| Runs on multiple platforms (Windows, Linux, UNIX) | Requires significant hardware resources for optimal performance | Ideal for data warehousing and business intelligence applications |
| Offers features like JSON support and data compression | Less community support compared to open-source databases | Used in healthcare for managing patient data and compliance |
| Provides advanced tools for data backup and disaster recovery | Requires skilled personnel for installation and tuning | Suitable for telecom companies managing customer data and billing |
MarieDB2
It is an open-source MySQL alternative aiming to provide speed and reliability. MariaDB supports many programming languages and is capable of handling loads of data very well. The features it offers include advanced security options, easy replication, and tools for performance tuning. Usually, MariaDB is used in web applications, data analytics, and in the cloud. Its user-friendly interface and strong community support make it popular with developers.
Key Features of Marie DB2
- Open-source with strong compatibility with MySQL.
- Enhanced performance and scalability for large datasets.
- Advanced security features, including encryption and user management.
- Support for multiple storage engines for flexible data handling.
- Rich indexing options for improved query performance.
- Built-in replication and clustering for high availability.
MariaDB is a powerful and flexible database solution that continues to be a top choice for businesses due to its regular updates and improvements.
| Marie DB2 Pros | Marie DB2 Cons | Use Cases |
| Fully open-source with no licensing fees | Fewer resources and community support than MySQL | Commonly used for dynamic websites and e-commerce platforms |
| Optimized for speed and efficiency | Some advanced features may be lacking in community versions | Suitable for analytical workloads and reporting |
| Easy migration from MySQL with similar commands and APIs | Scaling can be more complex in highly distributed environments | Used in popular CMSs like WordPress and Joomla |
| Offers features like data encryption, partitioning, and support for multiple storage engines | Forked from MySQL, which may lead to varying community support | Suitable for large-scale applications requiring reliability and advanced data handling |
| Continuous updates and improvements | Documentation may not cover all features in-depth | Used for cloud-based services needing robust and scalable databases |
Important: Unveiling Membase
The Final WordsÂ
Selecting the right DBMS is important for software developers to ensure data safety and efficiency. The DBMS options discussed above are among the most popular on the market for good reasons. Oracle excels in large-scale transactions with robust security, while MySQL is popular for web development due to its open-source nature. MongoDB is ideal for unstructured data and real-time application, and Redis offers high-speed performance for caching and real-time analytics. Choosing the right DBMS enhances application performance and data management.
By using these most used database systems, developers can improve their applications by making data management much easier while also accelerating project innovation. So, evaluate your needs and make informed decisions to achieve the desired results.
