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Explanation
Relational databases organize data into structured tables with predefined schemas and relationships. They are built around strong consistency guarantees and are well-suited for systems where data integrity and transactional accuracy are critical. In system design, choosing a relational database often implies careful upfront schema design to support current and future access patterns.
Schema design involves defining tables, columns, primary keys, and relationships in a way that minimizes redundancy while supporting efficient queries. Normalization is the process of structuring data to reduce duplication and maintain consistency. However, over-normalization can lead to excessive joins, increasing query latency. In 2026, system designers frequently balance normalized schemas with selective denormalization to optimize performance while preserving data correctness.
Table
Design Aspect | Benefit | Trade-off |
| Normalization | Data integrity | More joins |
| Indexing | Faster reads | Slower writes |
| Constraints | Consistency | Reduced flexibility |
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Example
An order management system uses a normalized schema with separate tables for users, orders, and products. This ensures that user details are stored once and referenced across multiple orders. As traffic grows, frequently accessed fields such as order status are selectively denormalized into the orders table to reduce join complexity. This approach maintains consistency while improving query performance.
Use Cases
Financial and payment systems
Inventory and order management
Applications requiring ACID transactions
Systems with complex relationships
Explanation
Non-relational databases are designed to scale horizontally and handle large volumes of data with flexible schemas. They trade some consistency or relational structure for scalability and performance. In system design, these databases are chosen based on access patterns rather than strict data relationships.
Key-value stores excel at fast lookups, document databases handle semi-structured data efficiently, and column stores are optimized for analytical workloads. In 2026, many systems adopt a polyglot persistence approach, using multiple database types within the same architecture to serve different needs. The key challenge is managing consistency and data synchronization across these stores.
Table
Database Type | Ideal Use Case | Strength |
| Key-Value | Caching, sessions | Low latency |
| Document | User profiles | Flexible schema |
| Column Store | Analytics | High read throughput |
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Example
A streaming platform stores user profiles in a document database to allow flexible attributes such as preferences and viewing history. Session data is stored in a key-value store for fast access, while viewing analytics are written to a column store for large-scale reporting. Each database is used where it performs best, resulting in a scalable and efficient system.
Use Cases
High-traffic consumer applications
Content management systems
Real-time analytics platforms
Systems requiring flexible data models
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