Streaming data is useful when a decision loses value while the data waits. It is unnecessary when the team only reviews the answer once a day. The right architecture starts with that distinction, not with a preferred platform.

Start with the decision

Pick one operating question and write down who uses the answer, what action follows, and how late the answer can arrive before it stops being useful. A dock-capacity alert may need fresh events. A weekly account review does not.

This first step prevents a common mistake: building a real-time pipeline for a reporting process that still runs on a daily or weekly cadence.

Choose the simplest cadence that works

Daily refresh

Use a daily load when the business reviews yesterday's activity, the source systems already produce reliable extracts, and a late correction will not change an immediate operating decision.

Frequent batch

Use smaller, more frequent loads when leaders need updates during the shift but do not need every source event as it happens. This often gives a 3PL enough freshness without the operating burden of a streaming platform.

Streaming events

Use change data capture or application events when the next action depends on a new scan, status change, or transaction arriving quickly. Streaming also makes sense when several downstream products need the same event history.

A practical architecture

Keep raw source records before applying business rules. Reconcile identifiers and timestamps in a separate layer. Build the operational view from those governed records, and retain enough lineage to show where each number came from.

Kafka, cloud queues, and open table formats can support this pattern, but they are implementation choices. The important design work is agreeing on event ownership, replay behavior, late-arriving data, and the definition of a corrected record.

Questions to answer before building

  • Which decision becomes better with fresher data?
  • What is the slowest acceptable update?
  • Which source owns each event and identifier?
  • How will corrections and duplicate events be handled?
  • Who will support the pipeline when a source system changes?

A good first project proves one decision path from source event to user action. If frequent batches solve the problem, stop there. If the operation truly needs streaming, the pilot will make that need clear.