How “real-time” is realistic without blowing budget?

For most decisions, P95 freshness under 60 seconds with >99.9% availability is enough. CDC + Kafka + modest Flink clusters + a right-sized serving store (Pinot/ClickHouse) will get you there without 7-figure spend. Reserve sub-10s for true edge cases (fraud, trading).

Can we do this on Snowflake/BigQuery only?

They’re excellent consumers and lakes but not serving layers for sub-second SLAs. Use them for modeling and historical analytics. For decisions with tight latency, keep a streaming path into a fast OLAP store or materialized view engine and mirror curated data to Snowflake/BigQuery.

Flink vs. ksqlDB vs. Spark Structured Streaming?

If you need heavy joins, exactly-once sinks, and scale, Flink is the safe bet. ksqlDB is simpler for Kafka-first shops. Spark is fine for micro-batch and unified batch/stream—but its latency floor is higher. Pick the tool your team can operate at 2 a.m.

What about schema evolution without outages?

Use Schema Registry with compatibility (BACKWARD or FULL). Version topics for breaking changes (`topic.v2`) and dual-publish during migration. Consumers can roll forward on their schedules.

How do we keep PII out of the wrong places?

Define data classes, segment topics by sensitivity, and enforce masking at ingestion. Use field-level encryption where needed. Keep PII out of DLQs by hashing or dropping fields before routing.

Data-engineering · Nov 10, 2025 · 10 minute read

Stop Faking Real‑Time: The Data Pipeline That Closes the CFO’s Tab, Not Your Pager

Real-time that actually drives decisions: contracts, CDC, stream processing, and ops discipline that keep dashboards right and teams sane.

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Stop Faking Real‑Time: The Data Pipeline That Closes the CFO’s Tab, Not Your Pager

Key takeaways

Implementation checklist