Real-time data processing has become a competitive necessity. Whether streaming user events, financial transactions, or sensor data, organizations need systems that process information the moment it arrives, not hours later.

Streaming architecture requires rethinking traditional batch processing approaches. Instead of collecting data and processing it in scheduled jobs, streaming systems process events as they arrive, enabling real-time responsiveness.

The challenge intensifies during peak loads. Live streaming events (like sports broadcasts or concerts) generate massive spikes in data. A single moment of slowness cascades through the entire pipeline, degrading user experience. Successful streaming systems handle these peaks gracefully.

Apache Kafka and Apache Pulsar have emerged as industry standards for event streaming. These platforms provide durable event storage, ordered processing, and exactly-once semantics—guarantees that traditional message queues struggle to provide.

Real-world streaming applications span entertainment (live video processing), finance (transaction monitoring), retail (inventory updates), and operations (log aggregation). Each domain has specific requirements for latency, throughput, and consistency.

Architecture patterns include event sourcing (storing all state changes as events), CQRS (command query responsibility segregation), and stream processing topologies that perform windowed aggregations, joins, and stateful transformations.

Modern approaches emphasize observability. When processing millions of events per second, understanding system behavior requires comprehensive metrics, structured logging, and distributed tracing. Without observability, bottlenecks are invisible.