How Data Contracts Work at Scale

The Scale Challenge: At companies processing thousands of events per second across hundreds of microservices, uncontrolled changes cascade into chaos. An ecommerce order service handling 5,000 writes per second publishes OrderPlaced events to both real time fraud models and batch analytics. Without a contract, renaming a field silently corrupts downstream aggregates within minutes at 10,000 events per second. The Contract Registry: Producers register schemas in a central catalog with machine readable compatibility rules. When the order service wants to change from amount in cents to dollars, the contract specifies "backward compatible changes only" with 90 day deprecation for breaking changes. The registry might be built on tools like schema registries that integrate with streaming platforms. Different SLAs for Different Needs: Fraud scoring needs events within 500 milliseconds p99 latency to block suspicious transactions. This drives architectural choices: streaming ingestion with low latency storage. Batch analytics only needs Timestamp plus 15 minutes freshness but requires 99.9 percent daily completeness. This allows cheaper batch ingestion. Runtime Enforcement: At ingestion time, validators check incoming data against contracts. For streaming systems, schema aware consumers reject or route invalid messages to quarantine topics. For batch warehouse loads, validation steps sample or scan data, checking schema, null rates, uniqueness constraints, and value ranges. LinkedIn and Netflix build observability layers computing Service Level Indicators (SLIs) like freshness, row counts, and anomaly scores on each dataset. Organizational Integration: Downstream applications declare dependencies on specific contract versions. A recommendation service states it depends on user_profile.v3 with p95 2 minute freshness and recent_activity.v2 with 99.5 percent completeness. Platform teams understand blast radius and prioritize which sources need strongest guarantees.