Which data integration pattern supports continuous updates?

• A. Batch processing
• B. Real-time streaming
• C. API-based integration
• D. Data archiving

Answer: (B)

Continuous updates come from a flow that delivers data changes as soon as they happen. Real-time streaming does exactly this by capturing events from sources and feeding them through a streaming platform so downstream systems see updates with minimal delay. This enables near-instant dashboards, alerts, and operational processes that rely on fresh information.

Batch processing, on the other hand, collects data over a period and processes it at set times, which introduces latency and isn’t suited for ongoing, up-to-the-minute updates. API-based integration can provide timely access, but it’s typically driven by requests or polling rather than a continuous, built-in stream of events. Data archiving focuses on storing historical data, not maintaining current state or continuous updates.