Part: 2 of 7: Data Without the Drama: Building Operational Trust in CDC Pipelines
If your only metric is “job running,” your AI is training on hope and good intentions.
So let’s start with the right signals. RDRS gives you deep operational information:
- Process status and lifecycle (L1)
- Latency, throughput, and replication statistics (L1)
- Error and failure reporting (L1)
Trust starts by putting these on the scoreboard.
Start with a handful of SLAs you can actually measure, then back them up with target-side validation that catches problems before your users (or your AI). These are the minimums to move from “it’s running” to “it’s reliable.”
SLAs (pick 3–5): Defining and operating to these SLAs is a practitioner responsibility (L2); RDRS provides the signals needed to measure them.
- P95 end-to-end lag ≤ X minutes (AI use cases usually need tighter)
- Success rate ≥ Y%
- Apply errors ≤ Z/day
- Process restarts/day
- Time above lag threshold
These are RDRS‑observable metrics.
Target-Side Validation (Not Performed by RDRS):
Validation happens after replication, using your warehouse, lake, or platform tools.
NOTE: Defining and owning these validation checks is a practitioner responsibility (L2), even though the checks are executed using downstream platforms and tools (L3).
Start simple:
- Row counts by window
- Key uniqueness
- Required fields non‑null
- Recon (if money or decisions depend on it)
KPIs AI Teams Will Ask For:
- P95 lag (and duration above threshold)
- Top apply errors and causes
- Restart frequency
- Time to detect and recover
If you can’t answer those, your AI team won’t trust the feed—no matter how fast it is.
Use the attached worksheet to document your current landscape: [att](Shift 1 Feed Trust Scorecard.docx|Shift 1 Feed Trust Scorecard.docx)
Your turn: Two minutes. 3 bullets. 4x value.
- What’s your current acceptable lag target(even if it’s unofficial)?
- Share one validation check you swear by: row counts, key uniqueness, required fields, recon, something else?
- What’s the one metric you wish you had but don’t?
Next week: Shift 2: Standardize Bulk + CDC + Fan-out
Chew on this with your squad before the next post: If you had to scale from a handful of AI feeds to dozens, what’s the first repeatable pattern or contract element you’d standardize so you’re not reinventing each pipeline?
Catch up on the series:
Can You Get from AI Demos to Systems You Can Actually Run?
Intro: Your AI Is Only as Real as Your CDC: 5 Shifts for Data Integration Practitioners
