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Every data pipeline starts with one question. Do you process data in chunks or as events arrive? This choice affects your tools and budget. Wrong choices cost money.
Batch processing collects data. It processes data on a schedule.
- It handles complex math well.
- It is easy to test.
- Data stays old between runs. Use it for revenue reports and ML training.
Streaming treats data as a flow. It processes events immediately.
- It triggers fast actions.
- It provides fresh data.
- It is hard to build. Use it for fraud detection and live sensors.
Ask one question. What happens when data is one hour old?
- No loss? Use batch.
- Business loss? Use streaming.
Some users want real-time but accept a 5 minute delay. Micro-batch runs on short intervals. It costs less than full streaming. It works for most dashboards.
Streaming costs 4x to 10x more to build. It needs more staff. You must manage state and data shifts.
Keep it simple.
- Action = Streaming.
- Analysis = Batch.
- Fast updates = Micro-batch.
Choose the simplest tool.
Source: https://dev.to/lucy1/batch-vs-streaming-pipelines-how-i-actually-choose-between-them-4fdn Optional learning community: https://t.me/GyaanSetuAi