2 min read Tech

7 Types of Databases: Complete 2025 Guide

Choosing the right database is crucial for your tech project's success. Performance, scalability, data structure: each type addresses specific needs. Discover the 7 essential databases.

1. Relational Databases (SQL)

SQL databases remain the reference for structured data in tables with relationships. PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server dominate the market.

Key Advantages:

  • ACID guarantee for data integrity
  • Complex relationships via foreign keys
  • Universal and mastered SQL language
  • Performance optimized by indexing

Main Limitation: Horizontal scalability remains complex due to relational constraints.

Use Cases: ERP, e-commerce, financial systems, CRM.

2. Columnar Databases

Designed for big data analytics, they store data by columns rather than rows, drastically accelerating analytical queries.

Leading Solutions:

  • Apache Cassandra: massive distribution
  • HBase: Hadoop ecosystem
  • Google BigQuery: managed data warehouse
  • Amazon Redshift: AWS solution

Benefits:

  • Scalability up to petabyte scale
  • Optimal data compression
  • Ultra-fast queries on specific columns
  • Ideal for machine learning

Use Cases: Log analysis, data warehousing, industrial IoT, BI dashboards.

3. Document Databases (NoSQL)

The JSON format offers maximum flexibility for agile projects requiring frequent schema modifications.

Market Leaders:

  • MongoDB: simplicity and performance
  • CouchDB: multi-device synchronization

Strengths:

  • Flexible schema without migration
  • Native horizontal scalability
  • Rapid structure evolution
  • Intuitive JSON queries

Warning: Lack of strict constraints can create inconsistencies if poorly managed.

Use Cases: Mobile applications, product catalogs, CMS, rapid prototyping.

4. Key-Value Databases

Redis and Memcached excel in caching thanks to RAM storage, offering millisecond response times.

Characteristics:

  • Ultra-low latency
  • Simple architecture
  • Queue and pub/sub support

Constraint: Capacity limited by available RAM. Ideal as complement to other databases.

Use Cases: Application cache, user sessions, real-time counters, gaming leaderboards.

5. Time Series Databases (TSDB)

Optimized for timestamped data, TSDBs excel in monitoring and trend analysis.

Specialized Solutions:

  • Prometheus: infrastructure monitoring
  • InfluxDB: high performance
  • Graphite: proven solution

Features:

  • Automatic time compression
  • Period aggregation
  • Native graphical visualization

Use Cases: IT monitoring, industrial IoT, financial data, weather sensors.

6. Graph Databases

Designed to model complex relationships, they outperform SQL for interconnected network analysis.

Main Players:

  • Neo4j: market leader
  • Amazon Neptune: AWS managed service
  • ArangoDB: multi-model

Decisive Advantage: Relationship traversal via direct pointers, eliminating costly SQL joins.

Use Cases: Social networks, fraud detection, recommendation engines, risk analysis, knowledge graphs.

7. Vector Databases

Emerging technology for AI and semantic search, storing embeddings for similarity comparisons.

Innovative Solutions:

  • Pinecone: cloud-native
  • Qdrant: high-performance open-source
  • Weaviate: AI semantic search

Principle: Text/image/audio transformation into vectors for meaning-based search, not exact keyword matching.

Use Cases: Semantic search engines, AI recommendations, image recognition.

How to Choose in 2025?

Decision Criteria:

  • SQL → ACID transactions, relational structure
  • Columnar → Big data, massive analytics
  • Document → Agility, rapid evolution
  • Key-Value → High-performance caching
  • TSDB → Time series, monitoring
  • Graph → Complex relationships priority
  • Vector → AI, semantic search

The choice depends on your consistency, volume, velocity, and data variety constraints. Often, a hybrid architecture combining multiple types offers the best solution.

Aurélien Meignen