Top 10 Most Popular Databases in 2024
In the ever-evolving landscape of data management, databases play a crucial role in storing, organizing, and retrieving information efficiently. As we approach 2024, the demand for robust and scalable database solutions continues to grow across various industries, especially IT industry.
It’s no surprise that database administrator roles are increasingly in demand in IT, with a yearly growth rate of 9% according to the US Bureau of Labor. With new job opportunities emerging regularly, newcomers often wonder which databases are most widely used in the industry.
From traditional relational databases to modern NoSQL and cloud-native options, the choices are vast and tailored to diverse needs. This article explores the ten most popular databases poised to dominate in 2024, highlighting their features, advantages, and notable use cases.
Key Takeaways:
- Oracle & IBM Db2: suitable for large enterprise applications, high security requirements, HA, multi-model – but very expensive.
- MySQL: popular, cheap, good for web, CMS, but lacks advanced extensions and enterprise.
- PostgreSQL: balance of features, performance, community; very flexible with OLAP, PostGIS extensions.
- MongoDB: reasonable for unstructured data and fast growth, but low DBaaS and ACID costs are limitations.
- Snowflake: leading data analytics, especially in the cloud; but high cost & vendor lock.
- Redis: ideal for caching, real-time messaging; not used as primary DB for important data.
- Elasticsearch: strong for searching and log analysis; used with analytics/search use‑case.
- SQLite: lightweight starter for mobile/embedded applications; not used for large workloads.
Evaluation criteria Database
Selecting the right database involves comparing key factors like popularity, scalability, performance, reliability, and features. This guide evaluates databases based on real-world use cases, ecosystem support, cost, and suitability for workloads like OLTP, OLAP, NoSQL, and caching.
- Popularity – based on DB‑Engines ranking (search, social, jobs…)
- Scalability – scalability by traffic/data
- Performance – throughput & latency
- Reliability & Durability – ACID, backup, HA assurance
- Feature Richness – data types, queries, extensions
- Ecosystem & Tooling – community, plugins, interfaces
- Cost & Licensing – operating costs, licensing
- Use‑case Suitability – OLTP/OLAP/NoSQL/caching/analytics
10 Most Popular Databases to Use in 2024
Relational databases are structured around tables, where data is organized in rows and columns. They use SQL (Structured Query Language) for querying and managing data. Here are the most popular relational databases to consider.
1. Oracle
Oracle is a widely acclaimed RDBMS written in C, C++, and Java assembly languages. The current iteration, Oracle Database 19c, stands as a testament to its ongoing evolution, although many organizations still utilize versions 11g and 12c. Renowned for its robust security and efficient data processing capabilities, Oracle Database also offers a comprehensive documentation.
2. MySQL
MySQL is a highly favored open-source RDBMS, prominently utilized by leading tech giants such as Facebook, Google, Twitter, and Adobe. It became part of Oracle through Sun Microsystems’ acquisition in 2009. Written in C and C++, MySQL employs structured query language (SQL). The latest iteration, MySQL 8.0, introduces enhanced recovery options.
3. MySQL Server
MySQL Server is a popular open-source relational database management system (RDBMS) developed by Oracle. It is used to store, manage, and retrieve data efficiently using Structured Query Language (SQL). MySQL is known for its speed, reliability, and ease of use, making it widely used in web development, especially with platforms like WordPress, PHP, and LAMP stacks.
4. PostgreSQL
This database is also an open-source Object-Relational DBMS that allows users the flexibility to create NoSQL databases as needed. Written in C, its popularity continues to grow steadily. It is particularly well-suited for organizations handling large volumes of data, making it a preferred choice for gaming apps, database automation tools, and domain registries. Major companies like Apple (which utilizes this database in macOS Server), Cisco, Fujitsu, Skype, and IMDb also rely on it. PostgreSQL operates seamlessly across multiple operating systems, including Windows, Linux, Solaris, and now Mac OS X.
PostgreSQL is versatile, suitable for both single-machine applications and large-scale internet-facing applications. It excels in creating fault-tolerant environments, managing data effectively, and ensuring data integrity.
5. MongoDB
MongoDB is a versatile NoSQL database that operates across multiple platforms. It is implemented using C++, C, and JavaScript programming languages. MongoDB finds applications in mobile apps, real-time analytics, and IoT environments, offering real-time access to all data. Data in MongoDB is stored in JSON-style documents, ensuring high-speed access through its use of internal memory. It excels in handling large volumes of data simultaneously.
6. Snowflake
Snowflake is a cloud-based data platform that provides data warehousing, analytics, and data sharing services. It allows businesses to store and analyze large amounts of structured and semi-structured data with high performance and scalability. Snowflake runs on cloud providers like AWS, Azure, and Google Cloud, and it separates storage and compute for flexible, cost-effective processing.
7. Redis
Redis, an open-source in-memory data structure store, supports a wide array of functionalities including database, cache, message broker, and streaming engine capabilities. It enables partial resynchronization during network splits and offers asynchronous replication. Redis leverages memory datasets for superior performance. Its focus on memory efficiency means that data structures implemented within Redis typically consume less memory compared to equivalents developed in higher-level programming languages.
8. Elasticsearch
Elasticsearch is a search engine built upon the Lucene library, known for its distributed and open-source nature, designed for full-text search and analytics. It features a distributed, multi-tenant capable architecture, offering a web interface over HTTP and supporting schema-free JSON documents. Many prominent organizations such as Wikipedia, The Guardian, StackOverflow, and GitHub utilize Elasticsearch.
9. IBM DB2
DB2, a standout offering from IBM, is a cloud-native database compatible with Windows, Linux, and Unix platforms. Designed to operate on IBM iSeries servers, this engine supports NoSQL functionalities like JSON and XML.
The latest iteration of this database engine introduces over 50 enhancements, featuring support for Machine Learning algorithms and Augmented Data exploration. With RMDA support, users can boost database performance by up to 40% and achieve 1.5x faster LOAD times.
10. SQLite
SQLite is the world’s most popular self-contained, serverless database management system provided as a software library. The name “lite” signifies its ease of setup, database management, and resource usage. SQLite can be deployed in diverse environments, including embedded devices such as iPhones, Android phones, gaming consoles, and portable media players. Currently, 14,523 companies utilize SQLite, commanding a market share of approximately 1.5%.
Choosing the right database depends on your project’s specific needs-whether it’s scalability, speed, structure, or flexibility. From robust relational systems like Oracle and MySQL to flexible NoSQL solutions like MongoDB and Redis, each option on this list offers unique strengths. As we move into 2025, these ten options stand out as the best database management systems for developers, businesses, and enterprises aiming to build efficient, scalable, and future-ready applications.
Compare Database Tools Ranking
| No | Database | Popularity/Rank | Type | Highlights |
| 1 | Oracle | 1 | RDBMS Multi-model | Enterprise-grade, ACID, multi-model, very powerful but expensive. |
| 2 | MySQL | 2 | RDBMS Multi-model | Easy to use, popular web, low cost; lacks some advanced features |
| 3 | MySQL Server | 3 | RDBMS Multi-model | Good BI/Windows integration, high security; closed, expensive on Linux |
| 4 | PostgreSQL | 4 | RDBMS Multi-model | Strong in standard SQL, PostGIS, extensions; large community |
| 5 | MongoDB | 5 | Document NoSQL | Flexible Schema‑less, Atlas DBaaS; high cost, limited ACID |
| 6 | Snowflake | 6 | Cloud Data Warehouse | Compute/storage separate, strong analytics; vendor-lock, costly |
| 7 | Redis | 7 | Key-value in-memory | Very fast, supports catching/pub-sub; needs additional DB for data persistence |
| 8 | Elasticsearch | 8 | Search & Analytics | Powerful, real-time search; complex, in-depth configuration |
| 9 | IBM Db2 | 9 | RDBMS, Multi-model | Good hybrid/enterprise; expensive, little outside community |
| 10 | SQLite | 10 | Embedded RDBMS | Zero-config, lightweight; not for large workloads |
Compare By Advantages and Disadvantages
| Use Case | Oracle | MySQL | PostgreSQL | MongoDB | Snowflake | Redis | Elasticsearch | IBM Db2 | SQLite |
| Web apps/ CMS | Very reliable but overkill & costly | ✅ Easy setup, widely used | ✅ Standard SQL, extensible | ✅ Flexible schema | ✖ Not suitable | ✖ Not ideal | ✖ Not suitable | ✅ Used in BI tools | ✅ Ideal for embedded/mobile apps |
| Enterprise / OLTP | ✅ High ACID, HA, clustering | ⚠️ Lacks advanced features | ✅ Enterprise features | ⚠️ Limited ACID support | ✖ Not suitable | ✖ Not suitable | ✖ Not suitable | ✅ Strong HA | ✖ Only for embedded use |
|
Analytics / DWH |
✅ Strong Oracle DW | ⚠️ Depends on external tools | ✅ OLAP extensions + DuckDB combo | ✖ Not suitable | ✅ Best for compute/storage separation | ✖ Cache only | ✅ Search + aggregation | ✅ Enterprise BI | ✖ Not suitable |
|
Real-time / Caching |
✖ Not primary purpose | ⚠️ Limited | ⚠️ Limited | ⚠️ Limited | ✖ Not suitable | ✅ Extremely fast | ⚠️ Not designed for caching | ⚠️ Limited | ✖ Not suitable |
|
Mobile / Embedded |
✖ Not suitable | ⚠️ Server-based | ⚠️ Server-based | ⚠️ Server-based | ✖ Not suitable | ⚠️ Cache only | ⚠️ Not suitable | ⚠️ Not suitable | ✅ Ideal lightweight embedded DB |
| Flexible Schema / JSON | ✅ JSON support | 🔸 Basic JSON | ✅ Better support + PostGIS | ✅ Very flexible | ✅ Semi-structured support | Limited | ✅ Text analytics & partial JSON | ✅ JSON support | ⚠️ Not suitable |
Choosing the Right Database
When selecting a database for your project, consider factors such as data structure, scalability requirements, performance needs, and the nature of your application. Each database type has its strengths and ideal use cases, so understanding these distinctions will help you make informed decisions.
Conclusion
As we look ahead to 2024, the landscape of database technologies continues to evolve rapidly, driven by the increasing demand for scalability, performance, and flexibility. Each of the databases highlighted in this article offers unique strengths and capabilities, catering to a wide range of application requirements and use cases. Whether you prioritize traditional relational databases like MySQL and PostgreSQL or opt for modern NoSQL solutions like MongoDB and Redis, the key lies in choosing a database that aligns with your specific needs for data storage, retrieval, and management. With ongoing advancements in cloud computing, artificial intelligence, and data analytics, the role of databases as foundational elements of digital infrastructure will only continue to expand in the years to come.
