When a CFO asks 'What's our burn rate today?' the answer needs to reflect today's data — not yesterday's batch load. Real-time streaming makes this possible, but it introduces architectural complexity that batch pipelines don't have.
Batch vs Streaming: When Each Makes Sense
Not every metric needs real-time data. Monthly revenue reports, quarterly forecasts, and HR headcounts are fine with daily batch loads. Real-time streaming is worth the complexity for metrics that change hourly: inventory levels, sales pipeline, production output, and cash position.
The Streaming Stack
A typical real-time pipeline: source systems emit change events (CDC), a streaming platform (Kafka, Pulsar) buffers and routes them, a stream processor (Flink, Spark Streaming) transforms and enriches, and the results land in a serving layer (Redis, ClickHouse) that the MCP semantic layer queries.
Latency and Freshness
Real-time doesn't mean instant. End-to-end latency from source event to queryable result typically ranges from 2-30 seconds depending on pipeline complexity. For conversational BI, this is more than sufficient — users perceive anything under 10 seconds as 'live'.
Cost Considerations
Streaming infrastructure costs 3-5x more than batch for the same data volume. The trick is selective streaming: only stream the tables and metrics that benefit from real-time freshness. Everything else stays on batch — and the semantic layer abstracts the difference so users don't notice.
Key Takeaways
- Batch vs Streaming: When Each Makes Sense
- The Streaming Stack
- Latency and Freshness
- Cost Considerations
Conclusion
Streaming infrastructure costs 3-5x more than batch for the same data volume. The trick is selective streaming: only stream the tables and metrics that benefit from real-time freshness. Everything els...
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