Sprint 30 Engineering Log

Program Context

• Phase: Phase 8
• Sprint: Sprint 30
• Status: Implemented (Awaiting Signed Commit Promotion)
• Primary Architecture Layers: Orchestrator Messaging Layer, Telemetry Ingestion Pipeline

Architectural Intent

Implement broker abstraction for RabbitMQ/Kafka compatibility, enforce ordered event streaming, and normalize telemetry into an ML-ready schema for high-throughput ingestion.

Implementation Detail

• Added Kafka-compatible routing and publishing contracts:
  • KafkaRoutingModel
  • InMemoryKafkaBroker
  • KafkaTelemetryPublisher
• Preserved RabbitMQ path and added strict ordering enforcement using:
  • ordering_key
  • stream_position
• Added telemetry schema normalization envelope for ML ingestion via ml_record payload with:
  • fixed telemetry.ml.v1 schema marker
  • status normalization (status_code)
  • stable feature keys and stream metadata
• Extended unit/integration coverage for:
  • broker abstraction behavior (RabbitMQ and Kafka)
  • ordering violation dead-letter behavior
  • normalized ML payload presence in emitted broker envelopes
• Added Sprint 30 high-volume QA test with 2,000-event throughput validation.

Security and Control Posture

• Broker transport remains decoupled from execution authorization controls.
• Ordered stream enforcement reduces replay/reordering ambiguity in downstream analytics.
• Normalized telemetry schema improves deterministic ingestion for ML pipelines.

QA and Validation Evidence

Validated with unit tests and QA artifact tests, including high-volume stream publishing.

Risk Register

Primary risk is functional drift between in-memory Kafka compatibility and production Kafka transport. Mitigation is to add a live Kafka adapter smoke suite in a follow-up sprint.

Forward Linkage

Sprint 31 extends this foundation with VectorVue cognitive feedback ingestion and policy-bound adaptation.