The urge to eliminate data duplication is a habit carried over from monolithic database design where normalization is king. In a microservices architecture, however, sharing identical or highly similar data across services requires a complete shift in perspective. If multiple services access the same database tables directly to read or write identical data, you have built a distributed monolith. You lose the primary benefits of microservices, which are independent deployability and isolated failure domains.
When two services share a database schema, any change to that schema requires coordinated deployments. If the billing service and the shipping service both write to the same customer table, a change in the billing logic can unexpectedly break the shipping system. The goal of microservices is to establish clear boundaries. To solve the problem of overlapping data, we must look to Domain-Driven Design and the concept of bounded contexts.
Even if data looks identical on the surface, its meaning and lifecycle differ depending on the service using it. A product entity in an inventory service contains warehouse locations and stock counts. That same product in a marketing service contains promotional copy and high-resolution images. While they refer to the exact same physical item, they are conceptually different. Trying to force these different perspectives into a single shared data model results in an overcomplicated schema that satisfies no single service well.
To handle truly overlapping data that must exist in multiple places, asynchronous replication is the preferred pattern. The service that owns the source of truth publishes an event whenever the data changes. Other services that need this data subscribe to these events and update their local, optimized read stores. This approach introduces eventual consistency, which is a necessary trade-off in distributed systems. Each service remains completely autonomous, keeping its own database offline from the others, and can continue to function even if the source service goes offline.
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Another option is direct API querying, where a service requests the necessary data on demand via gRPC or REST. This keeps data strictly in one place but introduces runtime dependencies and latency. You must weigh the network overhead and potential for cascading failures against the complexity of maintaining replicated data. Ultimately, duplicating data is almost always preferable to sharing a database because disk space is cheap, but engineering coordination and downtime are highly expensive.
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