Batch vs. Streaming: Choose the Right Processing Model

"We need real-time data." This is one of the most expensive sentences in data engineering — because it's rarely true, and implementing it when it's not needed multiplies complexity, cost, and operational burden. The question isn't "should we use streaming?" The question is "how fresh does the data actually need to be, and what are we willing to pay for that freshness?" The Question Isn't "Real-Time or Not" — It's "How Fresh?" Freshness requirements exist on a spectrum: Daily (24-hour latency): Fine for financial reporting, historical trend analysis, ML training datasets Hourly (1-hour latency): Adequate for operational dashboards, inventory tracking, marketing attribution Near-real-time (1-15 minutes): Sufficient for user activity feeds, recommendation updates, alerting Real-time (sub-second): Required for fraud detection, stock trading, IoT safety systems Most "we need real-time" requests are actually "we need hourly" or "we need 5-minute" requests. Clarifying the actual latency requi