What is a database? Modern Database Types, Examples, and Applications (2025)
In today’s data-driven world, databases form The backbone of modern applications– From mobile applications to enterprise systems. Understanding the different types of databases and their applications is essential to selecting the right system for a specific need, whether you are building a personal project or structuring an enterprise-level solution.
What is a database?
A database is a structured data collection that is stored electronically and managed by a database management system (DBMS). The database can be valid Storage, retrieval and management Both structured and unstructured data provide the basis for efficiently running applications.
Database selection significantly impact Performance, scalability, consistency and data integrity. Modern applications rely on databases to organize data and allow users to quickly and reliably access information.
Key Types of Modern Databases
1. Relational Database (RDBMS)
Relational Database Organize data into tables with rows and columns, using keys to execute patterns and relationships. They are acid-compliant (ensure atomicity, consistency, isolation, durability) and use SQL for data queries.
Recent Innovations (2025):
- mysql 9.0: Enhanced JSON processing, AI, vector data types for enterprise JavaScript stored procedures, SHA-3 encryption.
- Postgresql 17: Advanced JSON query capabilities, vector search ML, stream I/O, incremental backups, and more robust replication.
- Oracle Database and IBM DB2: Leading RDBMS in security, scalability, multi-cloud deployment and disaster recovery.
Best for: Financial systems, e-commerce, enterprise applications, analysis.
Popular Platforms: MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, IBM DB2, Mariadb.
2. NOSQL database
NOSQL database Apart from structured table-based models, providing flexible data formats suitable for semi-structured and unstructured data.
Key Types:
- Document Store: Store data as a JSON/BSON document. (For example, MongoDB, Couchbase)
- Key Value Store: Super fast, each data item is a key-value pair. (For example, Redis, Amazon DynamoDB)
- Wide store: Flexible columns per row; optimized for big data and analysis. (e.g., Apache Cassandra, HBase)
- Graph database: The node and edge model have complex relationships. (For example, Neo4J, Amazon Neptune)
- Multi-model database: Supports the above paradigms in a platform.
Significant progress (2025):
- mongodb: Now, using native enterprise SSO, Diskann vectors are used for production AI indexing, for horizontal scaling, powerful access controls.
- Cassandra 5.0: Advanced vector types for AI, storage attachment indexing, dynamic data masking and improved compaction of large-scale distributed workloads.
Best for: Real-time analysis, recommendation systems, Internet of Things, social platforms, streaming data.
3. Cloud Database
Cloud Database Manage on the cloud platform, providing resilience, high availability, hosting services and seamless scaling. They are optimized for modern DevOps and serverless environments and typically offer database as a service (DBAA).
Leading Platform: Amazon RDS, Google Cloud SQL, Azure SQL Database, MongoDB Atlas, Amazon Aurora.
Why choose the cloud?
- Automatic failover, expansion and backup.
- Global distribution can be used for high availability.
- A streamlined DEVOP with a managed infrastructure.
4. Memory and distributed SQL databases
Memory database (e.g., SAP HANA, SINGLESTORE, REDIS) Store data in RAM rather than disks for lightning access – ideal for real-time analytics and financial transactions.
Distributed SQL Database (e.g., Google Spanner) combines relational consistency (ACID) with NOSQL-style cloud scalability to handle multi-region deployments with global replication.
5. Time Series Database
Specially for storing and analyzing data in chronological order, such as sensor readings or financial scales. Optimized for fast ingestion, compression and time series queries.
Top Platforms: infuxdb, time scale.
6. Object-oriented and multi-model databases
- Object-oriented DB Like object DB maps directly to object-oriented code, it is perfect for multimedia and custom application logic.
- Multi-model database (e.g., Arangodb, Singlestore) can act as a document, key value, column storage, and graph database in one platform for maximum flexibility.
7. Professional and emerging types
- Ledger database: Immutable records and blockchain-like trust. (For example, Amazon QLDB)
- Search the database: Used for text search and analysis (e.g., Elasticsearch, OpenSearch).
- Vector database: Local indexing and retrieval of AI and search tasks, integrated with vector search and LLMS.
Highlights of cross-top platforms in 2025
| database | Recent Outstanding Features (2025) | Ideal use cases |
|---|---|---|
| mysql (rdbms) | JSON pattern verification, vector search, SHA-3, OpenID Connect | Web Applications, Analytics, AI |
| Postgresql | Vector search, stream i/o, json_table(), enhanced copying | Analytics, Machine Learning, Networking, ERP |
| mongodb | Local SSO, diskann index HD vector, powerful fragmentation | Cloud local, AI, content management |
| Cassandra | Vector type, new index, dynamic data masking, unified compaction | IoT, analytics, high-scale workloads |
| infuxdb | Extreme time series compression, Grafana integration, high throughput ingestion | Internet of Things, Monitoring, Time Series Analysis |
| DynamoDB | Serverless scaling, global replication, continuous backup | Real-time applications, serverless, network scale |
| cockroach | Cloud local, multi-region acid consistency, vector index (AI similarity search) | Global SQL, Fintech, Compliance |
| Mariadb | Columnary storage, MySQL compatibility, microsecond precision, advanced replication | Network, analysis, cloud |
| IBM DB2 | ML-driven tuning, multi-site replication, advanced compression | Enterprise, Analytics, Cloud/Hybrid |
Real-world applications
- E-commerce: Customers, Directories, Orders in RDBMS/NOSQL; Recommendation Engine in Graphics/Vector DB; Real-time Analysis in Time Series DB.
- banking: The core ledger in RDBMS; anti-fraud AI model relies on vectors and graph DB. Transactions in REDIS/in-memory cache.
- AI/ML: Modern DBs (e.g., MySQL, PostgreSQL, Cassandra, MongoDB) now support vector search and index LLMS, embedding and retrieval capabilities enhanced generation (RAG).
- IoT and surveillance: ImpruxdB, Cassandra handles millions of sensor readings in space and time for real-time dashboards.
Michal Sutter is a data science professional with a master’s degree in data science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels in transforming complex data sets into actionable insights.
