Which patterns are commonly used for real-time data exchange in CDX?

Which patterns are commonly used for real-time data exchange in CDX?

• A. Batch processing only.
• B. Pub/sub streaming, event-driven architectures, change data capture (CDC), and message queues with low-latency processing.
• C. Data warehousing overnight.
• D. Static file exchange.

Answer: (B)

Real-time data exchange relies on patterns that push data as soon as it changes. Pub/sub streaming lets producers publish events and consumers subscribe to receive them immediately, so updates flow to all interested parties without delay. An event-driven architecture coordinates system behavior around events; when something happens, a small, decoupled component reacts and triggers downstream processing, keeping latency low. Change data capture focuses on the altered data in a source and streams those changes to targets promptly, delivering only what’s new and speeding up synchronization. Message queues with low-latency processing provide reliable, ordered delivery and can absorb bursty traffic, letting different parts of the system process events in real time. Together, these patterns support real-time data exchange in CDX by enabling immediate propagation, decoupled reactions, efficient update streaming, and responsive processing. In contrast, batch processing processes data at fixed intervals, data warehousing overnight implies non-real-time ETL, and static file exchange typically lacks the continuous, event-driven flow necessary for real-time updates.

Real-time data exchange relies on patterns that push data as soon as it changes. Pub/sub streaming lets producers publish events and consumers subscribe to receive them immediately, so updates flow to all interested parties without delay. An event-driven architecture coordinates system behavior around events; when something happens, a small, decoupled component reacts and triggers downstream processing, keeping latency low. Change data capture focuses on the altered data in a source and streams those changes to targets promptly, delivering only what’s new and speeding up synchronization. Message queues with low-latency processing provide reliable, ordered delivery and can absorb bursty traffic, letting different parts of the system process events in real time.

Together, these patterns support real-time data exchange in CDX by enabling immediate propagation, decoupled reactions, efficient update streaming, and responsive processing. In contrast, batch processing processes data at fixed intervals, data warehousing overnight implies non-real-time ETL, and static file exchange typically lacks the continuous, event-driven flow necessary for real-time updates.