Shift 2: Standardize Bulk and CDC Patterns— Because AI at Scale Can’t Live on Bespoke Feeds
If Every Feed Is Special, None of Them Are Reliable
AI doesn’t scale on bespoke feeds. It scales on repeatable patterns and a predictable operating model.
Pick 2–3 RDRS-supported pattern families
- Initial bulk load followed by continuous CDC
- Capture once, replicate to multiple targets
- Continuous synchronization/coexistence patterns
RDRS supports these replication patterns, but selecting which patterns are allowed and enforcing consistency across teams are practitioner responsibilities outside of RDRS. That consistency is enforced through an explicit feed‑level contract (L2)*.
Standardize the “AI-ready contract” per feed (minimum)
- Keys + delete semantics
- Tier + SLAs (lag + correctness)
- Owner + on-call
- Validation bundle
- What changes trigger versioning
Kill click-ops: Use RDRS REST API for light orchestration: automate routine tasks you currently click through in the dashboard (health/status checks, process management actions, basic reporting). Keep complex workflows in your enterprise orchestrator if needed.
Note: RDRS provides automation interfaces (L1); deciding what to automate and how is a practitioner responsibility (L2), with orchestration typically handled by external tools (L3).
Use the attached worksheet to help you standardize patterns: [att](Shift 2_Standardize Bulk and CDC Patterns Checklist.docx|Shift 2_Standardize Bulk and CDC Patterns Checklist.docx)
Your turn: Two minutes. 3 bullets. High signal.
- Post your top 3 pattern types (e.g., bulk + CDC to warehouse, fan-out, streaming)
- Tell us what the most painful inconsistency is across teams today (e.g., delete semantics, keys, bulk loads, approach, monitoring).
- What is one manual dashboard task you’d love to delete from your life forever?
Next week: Shift 3: Sovereignty by Design
Chew on this with your squad before the next post: Where could an AI-critical feed land (especially in non-prod) that would create your biggest sovereignty/compliance problem today?
Catch up on the series: (links)
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
Shift 1: Make CDC Trustworthy (SLAs + Validation) — Because AI Hates “Maybe” Data
*Ownership and scope legend:
| L1 — RDRS Capability | What RDRS directly provides. |
| L2 — Practitioner responsibility (around RDRS) | What you must design, operate, and own to make RDRS outputs trustworthy. |
| L3 — External dependency (outside RDRS) | Important practices or capabilities not provided by RDRS. |
⚠️ L3 items are mentioned to emphasize that they happen outside of RDRS.
