ETL Validation Architecture
AI-Assisted Data Migration & Validation
ML-driven knowledge preparation feeds a fully deterministic validation pipeline — with gradual human-to-automation handoff modelled on CI/CD maturity.
⚡
FastAPI — Team Interface
Business Analysts
Product Owners
QA Engineers
triggers
Phase 1 — ML Data Preparation
📄
Knowledge Documents
Mapping · Dicts · Models · Specs
↓
🤗
HuggingFace Models
Chunking · Embedding generation
↓
FAISS Vector Index
Semantic vector store
↓
Ll
Meta Llama
RAG → YAML config generation
IDE + Cloud
↓
✅
Knowledge Layer — Ready
One-time setup · No runtime LLM in Phase 2
FAISS Index
YAML Config
Embeddings
🔄
CI/CD feedback loop: Doc changes trigger re-indexing → new embeddings → Llama regenerates updated YAML configs automatically. Future-proof as migration specs evolve.
Phase 2 — Deterministic Execution
Orchestration & Data
↓ queries
↓ vs
Validation Scripts
✓
Data Validation
Counts · nulls · types · formats
⚙
Business & Transform Rules
Logic · aggregations · mappings
Δ
Schema Drift
Column · type · index changes
Outputs
📊
Validation Report
HTML · JSON
✉️
Email Notify
SMTP · stakeholders
Knowledge layer activates Phase 2
Human-in-the-Loop → Gradual Automation
CI/CD maturity model
Early Stage
Auto-commit: OFF
Git schema snapshot taken
→ Human reviews diff
→ Manual approval gate
→ Commit on sign-off
→ Doc changes: human re-indexes
Mature Stage
Auto-commit: ON
Git schema snapshot taken
→ Auto-committed
→ Zero human gate
→ Doc changes trigger re-index
→ Llama regenerates YAML auto
ML Data Prep
HuggingFace
Meta Llama (RAG)
Deterministic (MCP + Python)
Human-in-the-Loop