Day 16: Explaining ML's Neglected Concepts - 𝗕𝗮𝘁𝗰𝗵 𝘃𝘀. 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Most tutorials teach you batch. Most jobs eventually need stream.
They're not just different speeds. They're different assumptions about when data shows up and how late a prediction can be before it's useless.
What actually happens:
•Batch collects data over a window, runs the job, then sits idle until the next one.
•Stream processes each event as it lands -> usually within milliseconds.
•The real difference isn't latency, it's how you handle state and what happens when something fails mid-job.
•Batch is cheaper and easier to reason about; stream is harder to build and harder to debug.
Key approaches in practice:
•Spark does batch well and stream via micro-batches, which isn't true streaming but covers a lot of cases.
•Flink is built for genuine event-time streaming with stateful operations across long windows.
•Kafka sits in front of both - it's a buffer, not a processor, and people mix this up constantly.
•Lambda architecture runs both in parallel, which sounds smart until you're maintaining two codebases forever.
What happens in real stacks:
•Fraud detection runs on stream -> by the time a batch job finishes, the transaction already cleared.
•Model retraining almost always stays batch; nobody retrains on every single row.
•Feature stores bridge the gap, serving precomputed batch features alongside live ones at inference time.
•Most teams start batch, add stream later, and spend months untangling the consistency issues that creates.
Have you ever had to migrate a batch pipeline to stream mid-production? How'd it go?
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