Streaming vs. Batch
Understand when to process data in batches versus streams, and how Staff+ engineers earn trust by choosing just enough speed to deliver business value.
Not all data needs to be real-time. Sometimes, a daily report is sufficient; other times, a five-minute delay can mean that fraud slips through the cracks or a user is double-charged.
Staff+ engineers earn trust by knowing when speed matters (and when it doesn’t). The choice between batch and streaming processing shapes both system cost and credibility.
Batch processing
Batch means collecting data for a period of time and processing it in chunks.
Pros: Simpler to build, cheaper to run, and reliable for reports and dashboards.
Cons: Delayed visibility; you only see results after the batch finishes.
Batch is great for weekly engagement reports, churn analysis, or metrics that don’t require minute-by-minute updates.
Streaming processing
Streaming handles data as it arrives, event by event.
Pros: Enables real-time alerts, fraud detection, and live personalization.
Cons: Complex to design, expensive to maintain, requires careful safeguards.
The golden rule is to only go streaming if the business case demands it. Don’t build it just to look fancy.
Batch vs. streaming comparison table
Dimension | Batch Processing | Streaming Processing |
Speed | Delayed; runs at scheduled intervals | Real time; processes events as they happen |
Complexity | Low; easy to build and maintain | High; requires careful design and safeguards |
Cost | Lower; compute/storage used less frequently | Higher; always-on systems cost more to operate |
Use Cases | Reporting, churn analysis, monthly metrics | Fraud detection, alerts, real-time personalization |
Team Impact | Fewer ops demands, easier for junior engineers | More operational overhead, requires stronger reliability focus |
Best When | Latency is tolerable and insights don’t need to be immediate | Business value depends on speed, accuracy, and immediacy |
How to choose an approach
Staff+ engineers resist the temptation to over-engineer. They choose “just enough speed” to deliver value without burdening the team with unnecessary ops overhead.
Choosing batch vs. streaming really comes down to trade-offs:
Batch: efficient, cost-effective, slower feedback.
Streaming: Immediate insights, high complexity, and higher costs.
As a best practice, build a batch when it’s sufficient, stream when necessary, and always explain the trade-off.
Let’s explore this decision with an example.
Example: Payments
Suppose you’re processing payments.
Batch: Aggregate all daily transactions and reconcile them with your payment provider nightly. This is simple and cost-effective.
Streaming: Process each transaction as it occurs, flagging real-time fraud attempts and sending instant confirmations to customers. This is complex, but critical; you can’t afford to double-charge or delay fraud detection.
Applied choice:
Batch is enough if you run a subscription service with modest volume and low regulatory risk.
Streaming is worth the extra complexity if you’re in fintech or e-commerce, where fraud prevention and instant feedback drive trust.