March, 2026
If you work in backend engineering, you might face one of difficult problem in distributed systems: Keeping two databases in sync.
As Gunnar Morling, creator of Debezium, famously put it: "Friends don't let friends do dual writes." Because dual-writes lack distributed transaction guarantees, they inevitably lead to partial failures and split-brain data drift.
Implementing a full Change Data Capture (CDC) pipeline is not a one-size-fits-all silver bullet. The architecture is driven by business constraints. Let’s break down the Outbox Pattern and its alternatives across four distinct scenarios.
Once you have identified the constraints with your product, it can significatly help the architecture decisions.
With the dual writes pattern, if your application crashes right affter writing a primary database, the secondary database will not be updated. This is a partial failure.
Can the business tolerate losing this event?
If you are tracking Ad impressions for an ML model, a 0.01% data loss rate could be acceptable. If you are processing financial ledger transactions or billing events, a single dropped event means lost revenue and regulatory fines. The Outbox pattern exists primarily to reduce this tolerance to zero.
In highly active systems, multiple users or processess might attempt to modify the exact same record at the exact same time.
Does your data model allow concurrent updates to the same entity?