Glossary

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Glossary / Evaluation and implementation guide

Data Streaming

Data streaming is a processing paradigm where events are transported and handled continuously as they occur, rather than collected and loaded in scheduled batches.

Core concepts include an event log or broker (such as Kafka-style systems), stream processors that transform events in motion, and consumers that write to warehouses or trigger actions.

A practical example

Example: product usage events stream from the application to a broker, where a processor enriches each event with plan data before writing to the warehouse and updating a lifecycle workflow.

What to evaluate before investing

  • Confirm the vendor supports at-least-once or exactly-once delivery and how duplicates are handled
  • Check ordering guarantees per key, since out-of-order events distort lifecycle sequences
  • Ask about replay: can you reprocess a past window of events after a schema fix?

Limitations and tradeoffs

Streaming pipelines add operational complexity, including monitoring lag, managing offsets, and handling schema evolution, that batch pipelines avoid.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.