Change Data Feed on Materialized Views Why I Think This Is More Than an Incremental Processing
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Sunday
One interesting way to look at CDF on Materialized Views is the difference between source-level change and business-level change.
CDC might tell us that five banking transactions changed. But after those transactions pass through our transformations, what a downstream consumer may actually care about is:
Risk Score: 42 → 67
Monthly Spend: $8,500 → $11,200
This raises an interesting architectural question: can our data products tell consumers not only their current state, but what changed since the last processing cycle?
I explored this using a banking Customer 360 example, including potential patterns for downstream processing and reconciliation, as well as why CDF should not be treated as a replacement for persistent audit history.
Full article: From Materialized Views to Change-Aware Data Products.
Lead Data Engineer | AI-Assisted Data Engineering
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Sunday
Good point, @AmitDECopilot that gap between "row changed" and "value actually changed enough to matter" is where most CDC setups trip up. We hit the same thing on a Gold-layer project: CDF told us data moved, but teams really wanted to know "did the number that matters cross a line" - like a risk score jumping into a new bucket. So we added a simple check that only alerts when that meaningful shift happens, not every time a row is touched.
One thing I'm curious about - when a source record comes in late or gets corrected, do you reprocess everything from raw again, or just patch the already-computed table directly?
