How do you synchronize data across heterogeneous systems?

Synchronizing data across heterogeneous systems involves ensuring consistency between systems that use different data formats, protocols, or storage technologies. The core approach typically combines standardized communication protocols, data transformation, and conflict resolution. For example, REST APIs or message brokers like Apache Kafka can facilitate data exchange between systems. Data transformation tools (e.g., Apache NiFi) or custom scripts convert data formats (JSON to XML, SQL to NoSQL) to match target system requirements. Conflict detection mechanisms, such as timestamp comparisons or version vectors, help resolve discrepancies when updates occur simultaneously in multiple systems.

A practical implementation might use a middleware layer to handle translation and routing. Suppose a retail application needs to sync inventory data between a legacy SQL database and a cloud-based NoSQL system. The middleware could poll the SQL database for changes, transform rows into JSON documents, and push updates to the NoSQL system via HTTP. For real-time sync, a change data capture (CDC) tool like Debezium could stream database changes to Kafka, where consumers process and forward them to downstream systems. Conflict resolution might involve a “last write wins” policy or merging changes based on business rules, such as prioritizing the source system with the most accurate data for specific fields.

Error handling and monitoring are critical for reliability. For instance, if a sync job fails due to a schema mismatch, the system should log the error, retry with exponential backoff, and alert developers. Tools like Prometheus and Grafana can track sync latency and success rates. To maintain data integrity, periodic checksums or audits compare subsets of data across systems. For example, a nightly job could verify that the total product count in a PostgreSQL database matches the count in Elasticsearch, flagging discrepancies for manual review. This combination of automation, validation, and observability ensures robust synchronization despite system differences.