<strong>Streaming data</strong> is processing data continuously as it arrives, rather than waiting for the entire dataset. It enables real-time analytics and reduces latency for large data transfers.
The Water Pipe Analogy
Streaming is like drinking from a faucet—you don't wait for the glass to fill completely. You start drinking as water flows. Same with data streaming.
Faucet (Data Source)
Continuous flow
Water Stream (Data)
Flows continuously
Glass (Consumer)
Processes as it arrives
Generates Data
Continuous Flow
Processes in Real-Time
Data arrives continuously
Sensor readings, user clicks, log entries
Stream processor receives
Kafka, Kinesis, or custom stream handler
Process immediately
Filter, transform, aggregate on the fly
Output results
Send to dashboard, database, or another stream
Never wait for 'complete' dataset
Process infinite streams in real-time
Wrong
"Streaming is only for video/audio"
Correct
<strong>Data streaming is different from media streaming</strong>. It's about processing continuous data flows: IoT sensors, financial transactions, user events, logs, etc.
Uber's real-time pricing:
Stream of ride requests and driver locations
Process stream to calculate surge pricing in real-time
Update prices every few seconds based on demand
Can't batch process—need instant pricing decisions