Introduction

Microservices have revolutionized how we build scalable, resilient, and independently deployable systems. Microservices is no doubt that microservices comes with a lot of benefit but is becomes tricky when you have to deal with databases and storage. One of the thorniest issues that arise when adopting microservice architecture is how to manage data across distributed services. Should each microservice own its own database? Should they share one? And how do you ensure consistency and reliability in a distributed world?

In this article, we’ll explore the major database architecture patterns in microservices, analyze their trade-offs, and offer a final verdict backed by real-world experience.

🔍 The Core Challenge: Data Ownership in Microservices

In a traditional monolith, all components of an application typically share the same database. But microservices flip this model by promoting bounded contexts — each service should be autonomous, owning its logic and data.

This leads to a key tension:

  • How do you isolate data for each service while still allowing meaningful collaboration between services?

  • What about cross-service queries, data consistency, and reporting?

🔄 Common Database Patterns in Microservice Architecture

Let's break down the most common patterns teams adopt:

1. 🗃️ Database per Service

Each service has its own private database, and no other service is allowed to access it directly.

✅ Pros:

  • Strong service autonomy
  • No accidental coupling via shared schemas
  • Services scale independently

❌ Cons:

  • Harder to perform joins across services
  • Eventual consistency requires extra effort
  • Cross-service reporting becomes complex

Use case:

Best for large systems where autonomy and scalability are key.

2. 🤝 Shared Database

All services access the same database and may even operate on the same tables. This is an anti-pattern it actually defeats the one of the two purpose of microservice which is scalability and modularity.

✅ Pros:

  • Simple to implement and query
  • Easy joins and reporting

❌ Cons:

  • Tight coupling of services
  • High risk of breaking changes
  • Poor scalability and autonomy

Use case:

Useful only in tightly coupled domains or legacy migrations.

3. 🧩 API Composition (Data Aggregator)

Services expose data via APIs, and an orchestrator (like an API gateway or BFF) aggregates the data.

✅ Pros:

  • Keeps data ownership intact
  • Flexible for building UIs and read models

❌ Cons:

  • Increased latency
  • Complexity in orchestrating requests

Use case:

Ideal for read-heavy services and UI composition layers.

4. 📚 CQRS + Event Sourcing

Use separate models for commands (writes) and queries (reads), often powered by event sourcing to track state changes.

✅ Pros:

  • High flexibility and scalability
  • Strong audit trail of events
  • Can optimize reads and writes independently

❌ Cons:

  • Steeper learning curve
  • Requires thoughtful schema and event design

Use case: Financial systems, order processing, audit-compliant platforms

📦 Change Data Capture (CDC) + Event-Carried State Transfer

Services publish database changes as events (using tools like Debezium + Kafka), and others consume and maintain local replicas of the data they need.

✅ Pros:

  • Maintains autonomy + availability of needed data
  • High-performance reads Works well with event-driven architecture

❌ Cons:

  • Potential for eventual consistency
  • Event schema evolution can be tricky

Use case: Cross-service read models, eCommerce, analytics dashboards

5. 📦 Change Data Capture (CDC) + Event-Carried State Transfer

Services publish database changes as events (using tools like Debezium + Kafka), and others consume and maintain local replicas of the data they need.

✅ Pros:

  • Maintains autonomy + availability of needed data
  • High-performance reads
  • Works well with event-driven architecture

❌ Cons:

  • Potential for eventual consistency
  • Event schema evolution can be tricky

Use case:

Cross-service read models, eCommerce, analytics dashboards

⚖️ Challenges Across the Board

No matter the pattern, some challenges are universal:

  • Data Duplication: Especially in CDC and CQRS
  • Consistency: You trade strong consistency for autonomy
  • Distributed Transactions: Sagas > 2PC in most microservice cases
  • Observability: Troubleshooting across services is harder
  • Schema Evolution: Updating shared data contracts is risky

🧠 Final Verdict

“Choose autonomy first. Go with a database per service model. Then use techniques like API Composition, Event Sourcing, and CDC to bridge the data needs.”

In most modern architectures, it's wise to keep services decoupled and eventually consistent. Over time, your system will benefit from:

  • Easier scaling
  • Faster team velocity
  • Lower risk of cascading failures

However, don't split your database too early — only when services truly require independence. For tightly coupled data and early-stage apps, shared databases might make sense temporarily.

Resources

  1. Microservices with Databases can be challenging Software Development Diaries
  2. Managing Data in Microservices – microservices.io by Chris Richardson Includes detailed breakdowns of database patterns like Database-per-Service, CQRS, and Saga.
  3. Event-Driven Architecture for Microservices – Confluent Focuses on CDC and Kafka with real-world examples.