Your dashboard shows yesterday's numbers. Your team made decisions this morning based on data that was already 18 hours old.
By the time you see a problem, it's already been a problem for hours.
You're always reacting to what already happened instead of responding to what's happening now.
The gap between "when it happened" and "when you knew" is where opportunities die.
INTERMEDIATE - Builds on message queues and event triggers to enable continuous data flow.
Streaming is the difference between checking your email once a day and having notifications pop up the moment something arrives. With batch processing, you collect data, wait, then process it all at once. With streaming, data flows through your system continuously, and you process each piece as it arrives.
Think about how you currently get reports. Someone runs a query. It pulls data from yesterday. You make decisions. But while you were deciding, the situation changed. Streaming closes that gap. Data enters your system and flows through immediately. Your dashboard updates. Your alerts fire. Your team sees what's happening, not what happened.
The value of data decreases over time. A customer complaint addressed in 30 seconds is a save. The same complaint addressed 24 hours later is a refund request. Streaming shrinks that gap from hours to seconds.
Streaming solves a universal problem: how do you react to events as they happen instead of discovering them later? Every business has moments where "knowing now" beats "knowing tomorrow."
Events enter a flow. Each event passes through processing steps immediately. Results appear downstream in real-time. No batching. No waiting. The gap between occurrence and visibility shrinks from hours to seconds.
Start generating events. Watch streaming react instantly while batch waits for its scheduled run. Alert triggers at 3 cancellations.
Data as a continuous flow
Instead of tables you query, you have streams you subscribe to. New data appends to the stream. Consumers read from wherever they left off. Multiple consumers can read the same stream independently.
Transform data as it flows
Processors sit on the stream and transform each event. Filter out noise. Enrich with context. Aggregate into metrics. Each processor handles one concern, and they chain together.
Deliver results immediately
Processed events flow to destinations that act on them: dashboards that update, alerts that fire, APIs that respond. The sink matches the urgency of the use case.
Your ops lead sees the alert within seconds of the third cancellation. They investigate, find a broken checkout flow, and fix it before customer #4 hits the same issue. Without streaming, you'd discover this in tomorrow's report, after 47 customers had the same problem.
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You rebuilt your entire reporting pipeline as streaming because the team wanted "live dashboards." Now you're spending 10x on infrastructure for reports that nobody looks at more than once a day. Real-time costs more. It's more complex. And most of your data doesn't need it.
Instead: Stream what needs immediate action (alerts, user-facing updates). Batch what just needs to be correct by morning (reports, reconciliation).
Your streaming pipeline handles 100 events per second beautifully. Then a busy period hits 10,000 events per second. Your processors can't keep up. Events pile up. Memory fills. The whole system crashes. You're now debugging at 2 AM.
Instead: Design for spikes from day one. Add queues as buffers. Build in rate limiting. Test with 10x expected load before going live.
A processor had a bug. It processed 50,000 events wrong. But those events are gone. They flowed through and you can't get them back. Now you're manually reconstructing data from logs, backups, and prayer.
Instead: Keep event streams for replay. Store raw events before processing. Build replay capability from day one. The storage cost is trivial compared to the recovery cost.
You've learned how continuous data flow differs from batch processing and when each makes sense. The natural next step is understanding how to store and query the time-stamped data that streaming produces.