All case studies
Data integration
Continuous data synchronization from MongoDB to Oracle
A Python integration service that captures MongoDB changes in real time and replicates them to Oracle, with exact resume after failures and gap reconciliation.
- Integrations
- Data synchronization
- Architecture
Context
Operational data was created in a MongoDB-based application, but other company systems — reports, routines and integrations — relied on it in Oracle. Periodic full loads were slow, left data stale and put load on both databases.
Challenge
- Deliver every change to Oracle with low latency, without reloading everything.
- Lose no events if the service, the network or either database went down.
- Handle volume and documents that don’t map directly to relational tables.
Approach
I architected a multithreaded Python integration service with asynchronous processing:
- Incremental capture (CDC) with MongoDB Change Streams: the service reacts to inserts, updates and deletes as they happen.
- Persisted resume tokens, so the service picks up exactly where it left off after a restart or failure.
- Recovery synchronization that reconciles periods when capture wasn’t running and closes any gaps.
- Idempotent upsert workers in Oracle: reprocessing an event never creates duplicates.
Outcome
- Oracle data continuously in step with MongoDB instead of periodic loads.
- Failure-resilient operation with automatic resume and reconciliation.
- A simple-to-operate architecture that can take on new collections and targets.