AI Kafka & CDC Planning

AI-assisted planning for Kafka infrastructure and CDC pipelines—topology choice, Connect sizing, and connector definitions.

Kafka · CDC · Debezium · AI · Architecture

Problem

Standing up CDC is more than “install Connect.” Topology, topic layout, key strategy, snapshot mode, and sink mapping all interact. Getting that wrong means idle sinks, broken keys, or overloaded brokers.

What it does

An AI-assisted planning layer takes structured inputs—source and sink engines, expected volume, lag budget, and preferred topology—and produces a blueprint: Connect placement, topic conventions, and source/sink connector JSON suitable for review and deploy.

Kafka CDC planning flowInputssources · volume · topologyAI blueprintsizing + configsPipelineConnect · topics · JSON
From requirements to a deployable Kafka Connect and CDC pipeline definition.

Topology choices

Typical patterns the planner reasons about:

  • Point-to-point — One source path to one sink; simplest operational model
  • Hub-and-spoke — Shared Kafka hub with multiple spokes
  • Active-active — Dual clusters with clear ownership of streams
  • Medallion / layered — Staged topics for bronze → silver style processing

Each choice drives different Connect URLs, bootstrap roles, and failure domains—described with role names only, never private hostnames.

Defining the CDC pipeline

The blueprint covers:

  • Snapshot versus streaming modes for initial load
  • Primary-key and key-strategy decisions for tables without stable keys
  • Wave-based inclusion lists for large schemas
  • Sink mapping when column or schema names differ

Operators review generated configs before anything is deployed to Connect.

Outcome

A repeatable path from “we need CDC between A and B” to an inspectable pipeline definition—aligned with the same comparison and migration workflows used in DBSync.