Every data pipeline makes one foundational decision before a single line of code is written.
Does it process data in scheduled chunks, or does it process data as events arrive?
That is the batch versus streaming decision. It looks simple on paper. In practice, it shapes everything: the tools you use, the infrastructure you maintain, the guarantees you can make about data freshness, and the cost you pay every month to keep it running.
Teams that build streaming pipelines when batch would have sufficed end up maintaining complex infrastructure for a problem that did not require it. Teams that build batch when their use case demands real-time discover the gap at the worst possible moment.
This post gives you a decision framework, a side-by-side comparison, and working code examples for both patterns. The full deep-dive with architecture diagrams, Lambda vs Kappa tradeoffs, and Databricks Real-Time Mode is at the original article on Lucent Innovation.
