Your inventory system syncs overnight. A customer orders at 9am.
The warehouse shows 12 units in stock. Reality: 0 units.
You just promised something you can't deliver.
The question isn't batch OR real-time. It's knowing which to use where.
DATA INFRASTRUCTURE - How you process data determines how fresh your decisions are.
Batch processing collects data over a period, then processes it all at once. Your nightly inventory sync. Your weekly sales report. Your monthly billing run. Everything happens on a schedule.
Real-time processing handles data as it arrives. Customer places an order? Inventory updates instantly. Payment confirmed? Shipping gets notified now. There's no waiting for tonight's sync.
Neither is universally better. Batch is simpler, cheaper, and handles large volumes efficiently. Real-time is faster but more complex and expensive. Most systems need both—the question is where to draw the line.
The cost of being wrong is different for inventory (angry customers) vs. monthly reports (nobody notices a 2-hour delay). Match the processing style to the business impact.
The fundamental trade-off between latency and throughput appears everywhere. Process frequently for speed, or accumulate for efficiency. The right answer depends on how much delay your use case can tolerate.
Identify your latency requirement first. If "stale by minutes" breaks something, go real-time. If "stale by hours" is fine, batch is simpler. Most systems have both—critical paths get real-time, everything else batches.
Sales and restocks happen throughout the day. See how your reported inventory drifts from reality based on your processing choice.
Click "Run Day" to start the simulation...
Scheduled bulk operations
Collect data throughout the day, process it all at once. A cron job runs at midnight, pulls all new orders, calculates commissions, and updates reports. Efficient for high volumes, but data is always somewhat stale.
Instant event handling
Process each event as it arrives. Order placed? Update inventory immediately. Payment received? Notify shipping now. No accumulation, no waiting. Data is always current.
The pragmatic middle ground
Process in small, frequent batches (every 30 seconds to 5 minutes). Get most of the freshness benefits without full real-time complexity. Often the right trade-off for analytics and reporting.
A big customer wants to place a 500-unit order. Your warehouse manager needs to know immediately if you can fulfill it. With nightly batch sync, you're guessing based on 12-hour-old data. With real-time inventory, you know the answer in 200ms.
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You rebuilt your weekly sales report as a real-time dashboard. It cost 3x more to build and 5x more to run. Nobody actually looks at it more than once a day anyway. You optimized for a latency requirement that doesn't exist.
Instead: Ask: "What breaks if this data is 1 hour old? 1 day old?" If nothing breaks, batch is probably fine.
Your inventory syncs every 4 hours. Between syncs, customers order products that are already sold out. Now you have angry customers and manual order cancellations. The "simple" batch approach is creating expensive problems.
Instead: Calculate the cost of stale data. If overselling costs $50 per incident and happens 20 times/day, real-time inventory is worth it.
Your nightly batch job processes 50,000 records. Row 47,832 has bad data. The whole job fails. You discover it at 9am when reports are missing. Now you're debugging a 6-hour-old problem under pressure.
Instead: Design for partial failure. Process in smaller chunks. Quarantine bad records. Alert on anomalies, not just failures.
You know how to choose between batch and real-time processing based on your latency requirements. The natural next step is learning how to aggregate data—whether that's summing up batches or rolling up streaming events.