Name three data integration patterns and describe them.

• A. Batch processing (periodic loads), real-time streaming (continuous updates), and API-based integration (data exchange via APIs).
• B. Data archiving, manual entry, and email transfer.
• C. Real-time streaming, encryption, and data masking.
• D. Batch processing only.

Answer: (A)

Data integration patterns describe how data moves and combines across systems to support timely, accurate insights. The best choice names three widely used patterns: batch processing, real-time streaming, and API-based integration. Batch processing collects data and loads it periodically, which is efficient for large volumes when up-to-date speed isn’t critical. Real-time streaming handles continuous data flows, pushing updates as soon as data is produced to support near-instant analysis. API-based integration leverages application programming interfaces to exchange data between systems on demand, enabling decoupled, flexible connections that can support various data formats and schedules. The other options mix activities that aren’t patterns of data integration (archiving, manual entry, or email transfer) or focus on security controls (encryption, data masking) or present only a single pattern, which doesn’t capture the concept of integrating data across systems.