What is Streaming Data

How It Works

Streaming systems process data records continuously as they arrive from producers. Records flow through processing stages (filtering, transformation, enrichment, aggregation) with minimal latency. Unlike batch processing that operates on complete datasets, streaming processes each record or micro-batch independently, enabling real-time or near-real-time results.

Technical Details

Platforms include Apache Kafka (distributed log), Apache Flink (stream processing), Apache Pulsar, and AWS Kinesis. Kafka provides durable, ordered message streams while Flink provides stateful computation over streams. Processing guarantees range from at-most-once to exactly-once semantics. Windowing operations (tumbling, sliding, session) group records for time-based aggregations. Throughput can reach millions of events per second.

Best Practices

• Choose the right processing guarantee (at-least-once vs exactly-once) based on application needs
• Implement backpressure handling to prevent overwhelming downstream consumers
• Use event time rather than processing time for accurate time-based operations
• Design for late data arrival with watermarks and allowed lateness policies

Common Pitfalls

• Assuming stream processing handles all use cases when batch is more appropriate for full reprocessing
• Not handling out-of-order events that arrive after their expected time window
• Ignoring state management in stateful stream processing, leading to memory issues
• Over-engineering with streaming when a simple polling approach meets latency requirements

Advanced Tips

• Use streaming to trigger real-time multimodal processing when new content is uploaded
• Implement stream processing for live video and audio analysis with frame-by-frame embedding
• Build lambda architecture combining batch and streaming for complete and timely results
• Use change data capture (CDC) streams to keep multimodal indices synchronized with source databases