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Master Data Management (MDM) is often overlooked until microservices start misbehaving due to data inconsistencies. If your services rely on customer, product, or vendor data and you're not managing it centrally, you're inviting chaos. Let's fix that.
? What Is Master Data in Microservices?
Master Data refers to the core, non-transactional data that defines business entities such as:
In microservices, each service owns its own data. But when multiple services need shared, consistent master data, things get complicated.
Common Pitfalls Without MDM
Common MDM Approaches in Microservices
1. Centralized MDM Service
Create a dedicated microservice that exposes APIs for master data (e.g., CustomerService, ProductCatalogService).
? Example:
GET /api/products/{id}
POST /api/customers
Pros:
Cons:
When to use: When master data rarely changes and strong consistency is needed.
2. Data Replication (Read-Only Caching)
Services consume master data via APIs or message queues and store local, read-only copies.
?️ Example:
Pros:
Cons:
When to use: High-read use cases (e.g., product catalogs, inventory).
3. Shared Database (Only for Master Data)
A separate shared database is used solely for master data, accessed by services via repositories or shared libraries.
Pros:
Cons:
When to use: In early-stage systems or when refactoring monoliths.
4. Domain Ownership & Reference Publishing
Each service owns its master data domain and publishes changes (via events) for other services to consume.
? Example:
Pros:
Cons:
When to use: In mature, distributed systems with asynchronous communication.
? Best Practices
Imagine an eCommerce system:
? Solution:
Master Data Management in microservices isn't just a technical choice—it's a business-critical strategy.
Choose your approach based on:
? Let’s Discuss
What MDM approach have you used in your microservice projects? Any horror stories or success patterns? Drop them below ?
? What Is Master Data in Microservices?
Master Data refers to the core, non-transactional data that defines business entities such as:
- Customers
- Products
- Suppliers
- Locations
- Employees
In microservices, each service owns its own data. But when multiple services need shared, consistent master data, things get complicated.
Common Pitfalls Without MDM
Customer name mismatch across systems- Product catalog inconsistencies
- Data duplication & syncing nightmares
- Hard-to-maintain integration logic
Common MDM Approaches in Microservices1. Centralized MDM Service
Create a dedicated microservice that exposes APIs for master data (e.g., CustomerService, ProductCatalogService).
? Example:
GET /api/products/{id}
POST /api/customers
Pros:
- Central source of truth
- Easy to control validation and versioning
- Ensures consistency
Cons:
- Becomes a bottleneck or single point of failure
- Doesn’t scale well for all domains
When to use: When master data rarely changes and strong consistency is needed.
2. Data Replication (Read-Only Caching)
Services consume master data via APIs or message queues and store local, read-only copies.
?️ Example:
- Product service pushes updates to Kafka
- Order service subscribes and caches product data locally
Pros:
- Highly available and decoupled
- Local read performance
Cons:
- Eventual consistency
- Requires sync/reconciliation mechanisms
When to use: High-read use cases (e.g., product catalogs, inventory).
3. Shared Database (Only for Master Data)
A separate shared database is used solely for master data, accessed by services via repositories or shared libraries.
Pros:
- Easy implementation
- Immediate consistency
Cons:
- Breaks microservice independence
- Tight coupling
When to use: In early-stage systems or when refactoring monoliths.
4. Domain Ownership & Reference Publishing
Each service owns its master data domain and publishes changes (via events) for other services to consume.
? Example:
- Customer Service publishes CustomerUpdated events
- Billing and Order services subscribe to updates
Pros:
- Loosely coupled
- Scalable and event-driven
- Easy to track changes
Cons:
- Eventual consistency
- Requires mature event architecture (Kafka, RabbitMQ, etc.)
When to use: In mature, distributed systems with asynchronous communication.
? Best Practices
- Define a canonical data model (avoid ambiguity)
- Use versioned APIs for master data
- Ensure idempotency for updates
- Use CDC (Change Data Capture) or event sourcing for sync
- Audit and monitor master data changes
- Automate reconciliation between services
Imagine an eCommerce system:
- ProductService: owns all product master data
- OrderService, CartService, InventoryService depend on it
? Solution:
- ProductService publishes product events to Kafka
- Others subscribe and cache locally
- Consistency ensured via periodic sync or hash-based reconciliation
Master Data Management in microservices isn't just a technical choice—it's a business-critical strategy.
Choose your approach based on:
- Frequency of change
- Consistency requirements
- Scalability expectations
- Team maturity
| Approach | Best For | Trade-offs |
|---|---|---|
| Centralized MDM Service | Strong consistency, simplicity | Bottleneck risk |
| Data Replication | Fast reads, loosely coupled systems | Eventual consistency |
| Shared DB for MDM | Simplicity in early stages | Breaks service independence |
| Event-Driven Ownership | Scalability, loosely coupled systems | Complex setup, requires maturity |
What MDM approach have you used in your microservice projects? Any horror stories or success patterns? Drop them below ?