<< back to Guides

🧠 Data Management Patterns

Data management patterns help architects and engineers design systems that are scalable, efficient, and maintainable by defining proven strategies for handling data storage, retrieval, consistency, and performance.

This guide covers essential data patterns used across distributed systems, analytics platforms, and high-throughput applications.

🧊 1. Cache Aside

🧠 Concept

The application checks the cache first. On a cache miss, it loads data from the database, stores it in the cache, and returns it to the caller.

// Pseudocode
if (cache.has(key)) {
  return cache.get(key);
} else {
  data = db.query(key);
  cache.set(key, data);
  return data;
}

✅ Use Cases

High-read, low-write scenarios
APIs with frequently accessed data
Microservices using Redis or Memcached

⚠️ Caveats

Must handle cache invalidation carefully
Risk of stale data if not synchronized

🪟 2. Materialized View

🧠 Concept

A Materialized View is a precomputed result of a SQL query stored on disk. It can be periodically refreshed to reflect changes.

// PostgreSQL example
CREATE MATERIALIZED VIEW active_users AS
SELECT id, name FROM users WHERE status = 'active';

✅ Use Cases

BI/Analytics dashboards
Aggregation-heavy workloads
Read-optimized architectures

⚠️ Caveats

Needs refresh logic (manual or scheduled)
Storage overhead

🪓 3. CQRS (Command Query Responsibility Segregation)

🧠 Concept

Separate models for:

Commands: create/update/delete (write)
Queries: read and fetch data

// Commands write to DB
POST /users → write store

// Queries use denormalized read store
GET /users → read store

✅ Use Cases

Systems with vastly different read/write loads
Event-driven or eventual consistency systems
Real-time dashboards vs transactional writes

⚠️ Caveats

Added complexity in syncing data
Often paired with Event Sourcing

🌀 4. Event Sourcing

🧠 Concept

Instead of storing the latest state, store all changes (events) as a log. Rebuild state by replaying events.

// Event log example
UserCreated {id: 1}
UserNameChanged {id: 1, name: "Bob"}
UserVerified {id: 1, verified: true}

✅ Use Cases

Systems requiring audit trails
Complex domains (financial, logistics)
Rollbacks, replays, debugging

⚠️ Caveats

High learning curve
Querying current state requires rebuilding
Event versioning and storage management

🧮 5. Index Table

🧠 Concept

A manually maintained table optimized for a specific query pattern. Acts as a custom secondary index.

// Example: speeding up user lookup by email
CREATE TABLE email_index (
  email TEXT PRIMARY KEY,
  user_id INT
);

✅ Use Cases

NoSQL or denormalized systems
Read-heavy queries on non-primary fields
Avoiding full table scans

⚠️ Caveats

Needs consistency logic (write-through, eventual)
Duplicated data

🧩 6. Sharding

🧠 Concept

Split large datasets into smaller subsets (“shards”) distributed across multiple servers or databases.

// Shard users by user ID modulus
user_id % 4 → shard_0 to shard_3

✅ Use Cases

Horizontal scaling
Geo-based partitioning
Large, multi-tenant systems

⚠️ Caveats

Complex query routing
Cross-shard joins are hard
Rebalancing shards is non-trivial

📂 Other Patterns to Know

🔁 Read Replicas

Use replica nodes for read traffic, keeping the master for writes. Good for scaling reads and improving latency.

💾 Write-Behind Caching

Update the cache first, and write to the database asynchronously.

🕒 TTL/Expiry Caching

Automatically remove data after a defined lifetime, useful for temporary or fast-changing content.

🔁 Summary Comparison

Pattern	Optimized For	Complexity	Notes
Cache Aside	Read latency	Low	Simple, needs cache invalidation
Materialized View	Aggregation queries	Medium	High speed reads, needs refresh strategy
CQRS	Scalability, separation	High	Often paired with Event Sourcing
Event Sourcing	Auditability, replay	High	Rebuild state from events
Index Table	Fast lookup	Medium	Denormalized data, requires sync logic
Sharding	Scalability	High	Partitioning for large-scale systems

📚 Further Reading

<< back to Guides