Why each service owns its data in Microservices - Why It Works This Way

Overview - Why each service owns its data

What is it?

In microservices architecture, each service is responsible for managing and storing its own data independently. This means no other service directly accesses or modifies another service's database. Each service owns its data to maintain clear boundaries and reduce dependencies.

Why it matters

Owning data independently prevents conflicts and confusion about who controls what information. Without this, services could overwrite each other's data, causing errors and making the system fragile. Clear data ownership helps teams work independently and scale the system smoothly.

Where it fits

Before this, learners should understand basic microservices concepts and database fundamentals. After this, they can explore data consistency patterns, event-driven communication, and distributed transactions.

Mental Model

Core Idea

Each microservice acts like a small business that keeps its own records and does not let others directly change them.

Think of it like...

Imagine a group of shops in a mall. Each shop keeps its own cash register and inventory list. No shop goes behind another's counter to change prices or stock. They communicate by sharing receipts or messages, not by touching each other's records.

┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│ Service A     │   │ Service B     │   │ Service C     │
│ ┌───────────┐ │   │ ┌───────────┐ │   │ ┌───────────┐ │
│ │ Database  │ │   │ │ Database  │ │   │ │ Database  │ │
│ └───────────┘ │   │ └───────────┘ │   │ └───────────┘ │
└───────┬───────┘   └───────┬───────┘   └───────┬───────┘
        │                   │                   │       
        ▼                   ▼                   ▼       
   Service A API       Service B API       Service C API
(Owns and controls)  (Owns and controls)  (Owns and controls)

Build-Up - 6 Steps

1

Foundation

Understanding Microservices Basics

Concept: Microservices are small, independent services that work together to form a larger application.

Microservices split a big application into smaller parts. Each part does one job well and can be developed, deployed, and scaled separately. This helps teams work independently and makes the system easier to maintain.

Result

You know that microservices break down big apps into smaller, manageable pieces.

Understanding microservices basics is essential because data ownership depends on clear service boundaries.

2

Foundation

What Data Ownership Means

Concept: Data ownership means a service is the only one allowed to read and write its own data store.

When a service owns its data, it controls how data is stored, updated, and accessed. Other services cannot directly change this data; they must ask the owning service to do it for them.

Result

You grasp that data ownership creates clear responsibility and control over data.

Knowing what data ownership means prevents confusion about who can change data and how.

3

Intermediate

Why Shared Databases Cause Problems

🤔Before reading on: do you think sharing one database among services makes coordination easier or harder? Commit to your answer.

Concept: Sharing a database between services creates tight coupling and risks data conflicts.

If multiple services use the same database, they can accidentally overwrite each other's data or cause inconsistent states. It also makes it hard to change one service without affecting others, slowing down development.

Result

You see that shared databases reduce independence and increase risk of errors.

Understanding the problems with shared databases explains why each service must own its data.

4

Intermediate

How Services Communicate Instead of Sharing Data

🤔Before reading on: do you think services should directly access each other's databases or communicate via messages? Commit to your answer.

Concept: Services exchange information through APIs or events, not by accessing each other's data stores.

Instead of reading or writing another service's database, a service calls the owning service's API or listens to events it publishes. This keeps data safe and boundaries clear.

Result

You understand that communication replaces direct data sharing to keep services independent.

Knowing communication patterns helps maintain data ownership and system stability.

5

Advanced

Handling Data Consistency Across Services

🤔Before reading on: do you think data consistency is easy or challenging when each service owns its data? Commit to your answer.

Concept: Maintaining consistent data across services requires careful design using eventual consistency and messaging patterns.

Since data is split, changes in one service may need to be reflected in others. This is done using events and asynchronous updates, accepting that data may be temporarily out of sync but will converge eventually.

Result

You realize that data ownership requires new ways to keep data consistent across services.

Understanding eventual consistency is key to designing reliable distributed systems with owned data.

6

Expert

Surprising Effects of Data Ownership in Large Systems

🤔Before reading on: do you think owning data always simplifies scaling, or can it introduce hidden complexities? Commit to your answer.

Concept: While data ownership improves independence, it can introduce challenges like complex data duplication and harder queries across services.

In large systems, owning data means some information is duplicated in multiple services for performance. Queries that need data from many services require careful design, often using APIs or data pipelines instead of direct joins.

Result

You appreciate that data ownership trades off simplicity in some areas for better modularity and scalability.

Knowing these trade-offs helps experts design systems that balance independence with performance.

Under the Hood

Each service has its own database schema and storage system. The service's code accesses only its database. Other services interact through network calls (APIs) or asynchronous messages (events). This separation ensures no direct database sharing, preventing conflicts and enabling independent scaling and deployment.

Why designed this way?

This design arose to solve problems of monolithic databases where changes by one team could break others. It supports team autonomy, faster development, and better fault isolation. Alternatives like shared databases were rejected due to tight coupling and scaling limits.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│ Service A     │       │ Service B     │       │ Service C     │
│ ┌───────────┐ │       │ ┌───────────┐ │       │ ┌───────────┐ │
│ │ Database  │ │       │ │ Database  │ │       │ │ Database  │ │
│ └───────────┘ │       │ └───────────┘ │       │ └───────────┘ │
└───────┬───────┘       └───────┬───────┘       └───────┬───────┘
        │                       │                       │       
        ▼                       ▼                       ▼       
   API / Events           API / Events           API / Events
        │                       │                       │       
        └───────────────┬───────┴───────┬───────────────┘       
                        ▼               ▼                       
                  Other Services   Other Services               

Myth Busters - 4 Common Misconceptions

Quick: Does owning data mean services never share any data at all? Commit yes or no.

Common Belief: Owning data means services keep all their data completely private and never share it.

Reality: Services do share data, but only through controlled APIs or events, not by direct database access.

Why it matters: Believing no data sharing happens can lead to designs that ignore necessary communication, causing incomplete or inconsistent information.

Quick: Is it easier to query data across services if they share one database? Commit yes or no.

Common Belief: Sharing one database makes querying data across services simpler and faster.

Reality: Shared databases create tight coupling and risk data corruption; cross-service queries are better handled via APIs or data pipelines.

Why it matters: Misunderstanding this can cause fragile systems that are hard to maintain and scale.

Quick: Does owning data guarantee perfect data consistency instantly? Commit yes or no.

Common Belief: If each service owns its data, data is always perfectly consistent everywhere immediately.

Reality: Data consistency is often eventual, meaning temporary differences exist until updates propagate.

Why it matters: Expecting instant consistency can lead to wrong assumptions and bugs in distributed systems.

Quick: Does data ownership mean no data duplication? Commit yes or no.

Common Belief: Owning data means no data is duplicated across services.

Reality: Some data duplication is common to improve performance and reduce cross-service calls.

Why it matters: Ignoring duplication needs can cause performance bottlenecks or complex data synchronization issues.

Expert Zone

1

Data ownership enables independent schema evolution, letting teams change their data models without coordination.

2

Eventual consistency requires designing idempotent and compensating operations to handle message retries and failures.

3

Data duplication must be carefully managed to avoid stale data and ensure timely updates across services.

When NOT to use

Data ownership is less suitable for tightly coupled systems needing strong immediate consistency or complex multi-entity transactions. In such cases, a monolithic or shared database approach or distributed transaction protocols may be better.

Production Patterns

In production, teams use patterns like API gateways, event sourcing, and CQRS to manage owned data. They implement asynchronous messaging with Kafka or RabbitMQ and use database-per-service to isolate failures and scale independently.

Connections

Domain-Driven Design (DDD)

Data ownership aligns with DDD's bounded contexts, where each context owns its data and logic.

Understanding bounded contexts helps grasp why data ownership is crucial for clear service boundaries.

Event-Driven Architecture

Data ownership relies on events to communicate changes between services asynchronously.

Knowing event-driven patterns clarifies how services keep data in sync without direct database sharing.

Organizational Team Structure

Data ownership mirrors Conway's Law, where team boundaries influence system architecture and data ownership.

Recognizing this connection helps design teams and services that align for better autonomy and faster delivery.

Common Pitfalls

#1 Allowing multiple services to write to the same database tables.

Wrong approach:

Service A and Service B both connect and update the same 'Orders' table directly.

Correct approach:

Only Service A owns and updates the 'Orders' table; Service B calls Service A's API to request changes.

Root cause:

Misunderstanding that data ownership means exclusive write access leads to tight coupling and data conflicts.

#2 Trying to perform complex joins across multiple service databases.

Wrong approach:

Writing SQL queries that join tables from Service A's and Service B's databases directly.

Correct approach:

Services query their own databases and share needed data via APIs or event streams; cross-service queries are done at the API level.

Root cause:

Assuming a shared database model in a microservices context causes architectural and operational problems.

#3 Expecting immediate consistency after updates in distributed services.

Wrong approach:

Designing user interfaces that assume data changes in one service instantly appear in others.

Correct approach:

Designing for eventual consistency, showing loading states or delays until data syncs across services.

Root cause:

Not accounting for asynchronous communication and network delays in distributed systems.

Key Takeaways

Each microservice must own and control its own data to maintain clear boundaries and reduce dependencies.

Direct database sharing between services causes tight coupling, data conflicts, and slows development.

Services communicate through APIs or events to share data safely without breaking ownership rules.

Data consistency across services is often eventual, requiring careful design of communication and updates.

Data ownership improves scalability and team autonomy but introduces trade-offs like data duplication and complex queries.