10 Must-Know Database Types for System Design Interviews

Choosing the right database is one of the most critical decisions in system architecture. Each database type is optimized for specific use cases — understanding them helps you design scalable, efficient, and reliable systems.

Whether you’re a backend developer or preparing for a system design interview, this guide covers 10 must-know database types to give you a competitive edge.

🔹 1. Relational Databases (RDBMS)

Examples: MySQL, PostgreSQL, Oracle
Relational databases store data in rows and columns with fixed schemas. Ideal for structured data with complex relationships.

✅ Use Cases: Financial apps, ERP, CRM, inventory systems
✅ Key Features: ACID compliance, strong schema enforcement

🔹 2. In-Memory Databases

Examples: Redis, Memcached
These databases store all data in RAM for ultra-fast read/write operations.

✅ Use Cases: Caching layers, session storage, real-time analytics
✅ Key Features: Millisecond response time, non-persistent by default

🔹 3. Key-Value Stores

Examples: DynamoDB, Riak, etcd
Simple, fast, and highly scalable for lookups using unique keys.

✅ Use Cases: User sessions, token storage, game leaderboards
✅ Key Features: High performance, schema-less

🔹 4. Document Stores

Examples: MongoDB, CouchDB
Store data in JSON or BSON format — flexible and schema-less.

✅ Use Cases: CMS, user profiles, product catalogs
✅ Key Features: Hierarchical data storage, deep querying

🔹 5. Graph Databases

Examples: Neo4j, Amazon Neptune
Designed for relationship-based queries using nodes and edges.

✅ Use Cases: Social networks, fraud detection, recommendation engines
✅ Key Features: Fast traversal, built-in graph algorithms

🔹 6. Wide-Column Stores

Examples: Apache Cassandra, Google Bigtable
Use tables, but store columns together for performance on write-heavy tasks.

✅ Use Cases: IoT, logs, metrics, real-time dashboards
✅ Key Features: Horizontally scalable, great for big data

🔹 7. Time-Series Databases

Examples: InfluxDB, TimescaleDB
Optimized for timestamped data with high write throughput.

✅ Use Cases: Monitoring systems, IoT sensors, financial data
✅ Key Features: Auto roll-ups, retention policies, fast aggregations

🔹 8. Text Search Engines

Examples: Elasticsearch, Solr
Built to handle full-text search at scale with powerful indexing.

✅ Use Cases: eCommerce search, logging systems, document archives
✅ Key Features: Fuzzy search, scoring, filters, NLP support

🔹 9. Spatial Databases

Examples: PostGIS (PostgreSQL extension), Oracle Spatial
Support spatial/geographic data types like points, shapes, coordinates.

✅ Use Cases: Maps, ride-sharing, logistics tracking
✅ Key Features: Geospatial queries, proximity search, routing

🔹 10. Blob Storage

Examples: Amazon S3, Azure Blob, MinIO
Not a traditional database, but widely used for storing binary objects (images, PDFs, backups).

✅ Use Cases: Media streaming, app storage, backups
✅ Key Features: High availability, durability, CDN support

📘 Summary Table

🎓 Learn More with These Courses:

🔗 IBM Data Science Professional Certificate
🔗 SQL Basics for Data Science
🔗 Google Business Intelligence Certificate
🔗 Coursera Data Science Courses

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