Source: data_layer/docs/ORGANIZATION_STRATEGY_COMPLETE.md

🎯 Complete Organization Strategy Analysis

Date: 2025-01-16
Purpose: Determine optimal folder organization: lifecycle vs slice vs scenario vs hybrid

📋 Current State Assessment

What You Have Now

data_fabric/
├── prompts/              # Lifecycle-ish (generation logic)
├── storage/              # Lifecycle (runtime operations)
├── knowledge/            # Lifecycle (intelligence operations)
├── kb_catalog/           # Mixed (business rules + config)
└── output-styles/        # Scenario-based (onboarding pipeline)
    ├── config/           # ⚠️ Doesn't fit "output-styles"
    ├── onboarding/       # ✅ Scenario-based stages
    └── schemas/          # ⚠️ Duplicates exist elsewhere

Current Organization: MIXED (70% lifecycle, 30% scenario)

🎨 Organization Philosophies Explained

1️⃣ Lifecycle Stage Organization

Definition: Organize by WHERE data exists in its transformation journey

data_fabric/
├── definitions/          # BIRTH: Canonical sources
├── weave/               # LIFE: Active processing
└── views/               # DEATH: Materialized outputs

Mental Model: Assembly Line

Raw materials → Processing → Finished goods

Pros:

✅ Clear data flow (source → runtime → output)
✅ Separation of concerns (immutable vs mutable)
✅ Git-friendly (know what to track vs ignore)
✅ Scalable (easy to add new lifecycle stages)
✅ DRY (single source of truth enforced)

Cons:

⚠️ Cross-cutting features span multiple stages
⚠️ Harder to navigate for feature-focused work
⚠️ Requires understanding of data lineage

Best For:

Data engineering teams
Systems with clear ETL pipelines
Multi-storage architectures
Version-controlled configuration

2️⃣ Slice/Domain Organization

Definition: Organize by WHAT business capability/domain it serves

data_fabric/
├── pricing/             # Everything pricing-related
│   ├── schemas/
│   ├── config/
│   ├── examples/
│   └── runtime/
├── scoring/
├── contracts/
└── questionnaires/

Mental Model: Vertical Slices

Each slice is self-contained

Pros:

✅ Feature co-location (everything for X in one place)
✅ Team ownership (clear boundaries)
✅ Easier feature work (don't jump directories)
✅ Microservice-ready (can extract slices)

Cons:

⚠️ Cross-domain code duplication risk
⚠️ Shared infrastructure unclear placement
⚠️ Inconsistent structure across slices
⚠️ Harder to see system-wide patterns

Best For:

Product-focused teams
Domain-driven design
Microservices architecture
Teams with ownership boundaries

3️⃣ Scenario/Workflow Organization

Definition: Organize by WHICH business process/workflow it supports

data_fabric/
├── onboarding/          # Everything for onboarding
│   ├── 01-ingest/
│   ├── 02-classify/
│   └── 03-contract/
├── analytics/
├── real_time_betting/
└── reporting/

Mental Model: User Journeys

Follow the business process

Pros:

✅ Business alignment (mirrors operations)
✅ Easy for stakeholders to understand
✅ Clear entry points for workflows
✅ Process optimization visibility

Cons:

⚠️ Massive duplication across scenarios
⚠️ Shared code unclear placement
⚠️ Rigid (hard to support new scenarios)
⚠️ Doesn't reflect code reuse

Best For:

Business-driven projects
Single workflow focus
Prototypes/MVPs
Process documentation

4️⃣ Hybrid Organization ⭐ RECOMMENDED

Definition: Lifecycle at top, domain/scenario within stages

data_fabric/
├── definitions/         # LIFECYCLE (immutable, git-tracked)
│   ├── schemas/         # Sliced by domain
│   ├── config/          # Sliced by domain
│   ├── templates/       # Sliced by scenario
│   └── examples/        # Sliced by scenario
│
├── weave/              # LIFECYCLE (runtime, operational)
│   ├── knowledge/       # Slice (intelligence)
│   ├── storage/         # Slice (persistence)
│   └── prompts/         # Slice (generation)
│
└── views/              # LIFECYCLE (outputs, gitignored)
    ├── onboarding/      # Scenario-based
    ├── contracts/       # Scenario-based
    └── analytics/       # Scenario-based

Mental Model: Layered Cake with Flavors

Layers = lifecycle stages
Flavors = domains/scenarios within

Pros:

✅ Best of both worlds (clear flow + feature co-location)
✅ Flexible (choose organization per layer)
✅ Intuitive (lifecycle for infra, domain for business)
✅ Scalable (add slices without restructuring)

Cons:

⚠️ More complex (two organization principles)
⚠️ Requires discipline (don't mix metaphors)

Best For:

Complex systems with multiple concerns
Mixed technical/business focus
Growing teams
YOUR SYSTEM ✅

🎯 Decision Matrix for YOUR System

Your System Characteristics

Characteristic	Reality	Org Implication
Multi-storage	PostgreSQL + Redis + Vector	→ Lifecycle (separate runtime)
Multiple workflows	Onboarding, analytics, contracts	→ Scenario (within outputs)
Business domains	Pricing, scoring, sports	→ Slice (within config)
Auto-generation	Config → Examples, Schema → Adapters	→ Lifecycle (source vs derived)
Team size	Small/Growing	→ Hybrid (room to evolve)
Git management	Version control critical	→ Lifecycle (immutable vs gitignore)

Conclusion: Hybrid Organization (Lifecycle + Domain/Scenario)

🏗️ Recommended Structure (Complete)

Top-Level: Lifecycle Stages

data_fabric/
├── definitions/         # 🔒 Git-tracked, immutable, canonical
├── weave/              # 🔧 Python modules, operational code
├── views/              # 📊 Generated outputs, gitignored
├── docs/               # 📚 Documentation (lifecycle-agnostic)
├── scripts/            # 🛠️ Maintenance utilities
└── tests/              # ✅ Testing (lifecycle-agnostic)

Level 1: `definitions/` - SOURCE OF TRUTH

Organization: DOMAIN-SLICED (by business capability)

data_fabric/
└── definitions/                          # All canonical data
    │
    ├── schemas/                          # ✅ Keep current structure
    │   ├── domain/v1/                    # Domain models
    │   │   ├── league/
    │   │   ├── sports/
    │   │   ├── contract/
    │   │   ├── pricing/
    │   │   └── questionnaire/
    │   │
    │   ├── generated/                    # Auto-generated adapters
    │   │   ├── drizzle/                  # TypeScript/Drizzle
    │   │   ├── pydantic/                 # Python/Pydantic
    │   │   └── typescript/               # TypeScript interfaces
    │   │
    │   └── README.md
    │
    ├── config/                           # Domain-specific business rules
    │   ├── business/
    │   │   ├── pricing/                  # ← MOVE FROM output-styles/config
    │   │   │   ├── tier_presets.v1.json
    │   │   │   ├── combat.pricing.v1.json
    │   │   │   ├── default.pricing.v1.json
    │   │   │   └── README.md
    │   │   │
    │   │   ├── scoring/                  # ← MOVE FROM output-styles/config
    │   │   │   ├── scoring_model.v1.json
    │   │   │   ├── weights.v1.json
    │   │   │   └── README.md
    │   │   │
    │   │   └── contracts/                # NEW: Contract templates config
    │   │       ├── template_mappings.json
    │   │       ├── clause_library.json
    │   │       └── README.md
    │   │
    │   ├── sports/                       # Sport-specific configs
    │   │   ├── archetypes.json
    │   │   ├── betting_markets.json
    │   │   ├── data_requirements.json
    │   │   └── README.md
    │   │
    │   ├── pipeline/                     # Pipeline stage configs
    │   │   ├── onboarding_stages.json
    │   │   ├── validation_rules.json
    │   │   └── README.md
    │   │
    │   └── README.md                     # Config governance
    │
    ├── templates/                        # SCENARIO-ORGANIZED (by workflow)
    │   ├── prompts/                      # AI prompt templates
    │   │   ├── onboarding/
    │   │   │   ├── extract_questionnaire.j2
    │   │   │   ├── classify_sport.j2
    │   │   │   └── suggest_tier.j2
    │   │   │
    │   │   ├── contracts/
    │   │   │   ├── generate_terms.j2
    │   │   │   └── assemble_document.j2
    │   │   │
    │   │   ├── components/               # Reusable fragments
    │   │   │   ├── system_instructions/
    │   │   │   ├── output_formats/
    │   │   │   └── few_shot/
    │   │   │
    │   │   └── README.md
    │   │
    │   └── contracts/                    # Document templates
    │       ├── term_sheet.md.j2
    │       ├── msa.md.j2
    │       └── README.md
    │
    └── examples/                         # SCENARIO-ORGANIZED (training data)
        ├── onboarding/
        │   ├── questionnaire_extraction/
        │   │   ├── examples.jsonl        # Manual examples
        │   │   ├── metadata.json
        │   │   └── README.md
        │   │
        │   ├── tier_classification/
        │   │   ├── examples.jsonl        # Manual examples
        │   │   ├── generated.jsonl       # ← AUTO-GENERATED from config
        │   │   ├── generator.py          # ← Generation script
        │   │   └── README.md
        │   │
        │   └── contract_assembly/
        │       ├── examples.jsonl
        │       └── README.md
        │
        ├── sports_classification/
        │   ├── by_archetype.jsonl
        │   ├── by_market_readiness.jsonl
        │   └── README.md
        │
        └── README.md                     # Example governance

Why Domain-Sliced Here:

✅ Config naturally groups by domain (pricing, scoring)
✅ Schemas already domain-organized
✅ Templates group by use case (scenario)
✅ Examples group by training task (scenario)

Level 2: `weave/` - OPERATIONAL RUNTIME

Organization: TECHNICAL-SLICED (by system capability)

data_fabric/
└── weave/                                # All runtime operations
    │
    ├── knowledge/                        # ✅ Keep structure (AI operations)
    │   ├── __init__.py
    │   ├── embeddings/                   # Vector generation
    │   │   ├── __init__.py
    │   │   ├── service.py
    │   │   └── config.py
    │   │
    │   ├── intent/                       # Query classification
    │   │   ├── __init__.py
    │   │   ├── classifier.py
    │   │   └── patterns.py
    │   │
    │   ├── retrieval/                    # RAG operations
    │   │   ├── __init__.py
    │   │   ├── rag_service.py
    │   │   ├── query_builder.py
    │   │   └── reranker.py
    │   │
    │   ├── storage/                      # Vector DB interface
    │   │   ├── __init__.py
    │   │   ├── langmem_client.py
    │   │   └── vector_store.py
    │   │
    │   └── templates/                    # Dynamic prompt assembly
    │       ├── __init__.py
    │       ├── prompt_builder.py
    │       └── template_loader.py
    │
    ├── storage/                          # ✅ Keep structure (persistence)
    │   ├── __init__.py
    │   ├── examples/                     # ⚠️ This is CODE, not data!
    │   │   ├── __init__.py
    │   │   ├── retriever.py             # Example retrieval system
    │   │   ├── matcher.py               # Example matching logic
    │   │   ├── cache.py                 # Runtime example cache
    │   │   └── data/                     # .gitignore runtime cache
    │   │
    │   ├── postgres/                     # PostgreSQL operations
    │   │   ├── __init__.py
    │   │   ├── client.py
    │   │   └── models/
    │   │
    │   ├── redis/                        # Cache layer
    │   │   ├── __init__.py
    │   │   └── client.py
    │   │
    │   └── supabase/                     # Supabase operations
    │       ├── __init__.py
    │       └── client.py
    │
    ├── prompts/                          # ✅ Enhance (generation logic)
    │   ├── __init__.py
    │   ├── builders/                     # Prompt construction
    │   │   ├── __init__.py
    │   │   ├── onboarding_prompts.py
    │   │   ├── classification_prompts.py
    │   │   ├── contract_prompts.py
    │   │   └── base.py
    │   │
    │   ├── registry/                     # Prompt metadata
    │   │   ├── __init__.py
    │   │   └── catalog.json
    │   │
    │   └── README.md
    │
    ├── generators/                       # NEW: Data generation pipelines
    │   ├── __init__.py
    │   ├── config_to_examples.py         # Config → Examples
    │   ├── schema_to_adapters.py         # Schema → Pydantic/TS
    │   └── contract_assembler.py         # Data → Contracts
    │
    └── validators/                       # NEW: Validation logic
        ├── __init__.py
        ├── schema_validator.py
        ├── config_validator.py
        └── example_validator.py

Why Technical-Sliced Here:

✅ Python modules are technical capabilities
✅ Clear separation of concerns (knowledge vs storage vs generation)
✅ Easy to test (mock boundaries)
✅ Reusable across scenarios

Level 3: `views/` - MATERIALIZED OUTPUTS

Organization: SCENARIO-BASED (by business workflow)

data_fabric/
└── views/                                # ⚠️ .gitignore entire directory
    │
    ├── onboarding/                       # Onboarding pipeline outputs
    │   ├── 02-ingest-validate-questionnaire/
    │   │   ├── example_seeds/            # Input seeds
    │   │   ├── validated/                # Validation results
    │   │   └── metadata/                 # Processing metadata
    │   │
    │   ├── 03-enhance-documents/
    │   │   ├── enriched/
    │   │   └── metadata/
    │   │
    │   ├── 04-classify-and-score/
    │   │   ├── classifications/
    │   │   ├── scores/
    │   │   └── recommendations/
    │   │
    │   ├── 05-upsert-and-crossref/
    │   │   ├── upserted/
    │   │   └── relationships/
    │   │
    │   ├── 06-suggest-tiers-and-terms/
    │   │   ├── tier_suggestions/
    │   │   ├── term_suggestions/
    │   │   └── pricing_recommendations/
    │   │
    │   ├── 07-assemble-contract/
    │   │   ├── drafts/
    │   │   ├── final/
    │   │   └── metadata/
    │   │
    │   ├── 07a-output-contract-export/
    │   │   ├── pdf/
    │   │   ├── docx/
    │   │   └── markdown/
    │   │
    │   ├── 07b-output-gamekeeper-scorekeeper-ui/
    │   │   ├── configs/
    │   │   └── data/
    │   │
    │   └── 07c-output-marketing-nxt-onboarding-materials/
    │       ├── presentations/
    │       └── assets/
    │
    ├── analytics/                        # Analytics pipeline outputs
    │   ├── reports/
    │   ├── dashboards/
    │   └── exports/
    │
    ├── contracts/                        # Generated contracts (all workflows)
    │   ├── term_sheets/
    │   ├── msas/
    │   └── amendments/
    │
    └── uploads/                          # User-uploaded files
        ├── questionnaires/
        └── documents/

Why Scenario-Based Here:

✅ Business workflows are scenarios
✅ Each pipeline stage produces artifacts
✅ Easy to clean up (rm -rf views/)
✅ GitIgnored (don't track generated files)

📊 Comparison: Current vs Recommended

Current Structure Issues

data_fabric/
├── output-styles/                    # ❌ Mixed metaphor
│   ├── config/                       # ❌ Should be in definitions/
│   ├── onboarding/                   # ✅ Good (scenario-based)
│   └── schemas/                      # ❌ Duplicate of schemas/
│
├── prompts/                          # ⚠️ Mixed (templates + code)
│   ├── components/                   # ✅ Should be in definitions/
│   └── builders/                     # ✅ Should stay (code)
│
├── kb_catalog/                       # ⚠️ Unclear purpose
│   ├── constants/                    # ✅ Good (business rules)
│   └── manifests/                    # ⚠️ What's this?
│
└── storage/examples/                 # ❌ Confusing (code or data?)

Problems:

Mixed lifecycle stages (source + runtime + output)
Duplicate schemas (schemas/ and output-styles/schemas/)
Unclear metaphors ("output-styles" but has config?)
Code vs data confusion (storage/examples/ is code!)

Recommended Structure Benefits

data_fabric/
├── definitions/                      # ✅ Clear: "source of truth"
│   ├── schemas/                      # ✅ Only place for schemas
│   ├── config/                       # ✅ Only place for business config
│   ├── templates/                    # ✅ Only place for templates
│   └── examples/                     # ✅ Only place for training data
│
├── weave/                           # ✅ Clear: "operational code"
│   ├── knowledge/                    # ✅ AI operations
│   ├── storage/                      # ✅ Persistence operations
│   ├── prompts/                      # ✅ Generation code
│   └── generators/                   # ✅ Transformation code
│
└── views/                           # ✅ Clear: "generated outputs"
    ├── onboarding/                   # ✅ Scenario-based
    └── analytics/                    # ✅ Scenario-based

Benefits:

✅ Single source of truth (no duplicates)
✅ Clear lifecycle (definitions → weave → views)
✅ Git-friendly (track definitions, ignore views)
✅ Domain-sliced where it matters (config, schemas)
✅ Scenario-sliced where it matters (pipelines, examples)

🔄 Migration Strategy

Phase 1: Non-Breaking Additions (Week 1)

# Create new structure without deleting old
mkdir -p data_fabric/definitions/{schemas,config,templates,examples}
mkdir -p data_fabric/definitions/config/{business,sports,pipeline}
mkdir -p data_fabric/definitions/templates/{prompts,contracts}
mkdir -p data_fabric/definitions/examples/onboarding
 
mkdir -p data_fabric/weave/{knowledge,storage,prompts,generators,validators}
 
mkdir -p data_fabric/views/{onboarding,analytics,contracts,uploads}

Phase 2: Copy (Don't Move) Critical Files (Week 1)

# Config files (keep originals as backup)
cp -r data_fabric/output-styles/config/business/* data_fabric/definitions/config/business/
 
# Prompt templates
cp -r data_fabric/prompts/components/* data_fabric/definitions/templates/prompts/components/
 
# Examples (if any exist outside storage/)
# ... identify and copy

Phase 3: Update Import Paths (Week 2)

# OLD
from database.output_styles.config.business.pricing import tier_presets
 
# NEW
from data_fabric.definitions.config.business.pricing import tier_presets

# Find all references
grep -r "output_styles.config" data_fabric/ --include="*.py"
grep -r "from database" data_fabric/ --include="*.py"
 
# Automated replacement
find data_fabric -name "*.py" -type f -exec sed -i '' \
  's/from database\.output_styles\.config/from data_fabric.definitions.config/g' {} +

Phase 4: Test & Validate (Week 2)

# Run all tests
python -m pytest data_fabric/tests/
 
# Validate imports
python -c "from data_fabric.definitions.config.business.pricing import tier_presets"
 
# Check for broken imports
python scripts/check_imports.py

Phase 5: Delete Old Structure (Week 3)

# Only after confirming everything works!
git rm -r data_fabric/output-styles/config/
git rm -r data_fabric/prompts/components/  # Move to definitions/templates
 
# Update .gitignore
echo "data_fabric/views/*" >> .gitignore
echo "!data_fabric/views/README.md" >> .gitignore

🎯 Special Considerations

1. `kb_catalog/` - Where Does It Go?

Current Location: Top-level (unclear)

Options:

Option A: Merge into definitions/config/

definitions/
└── config/
    ├── business/         # Business rules
    ├── sports/           # Sports config
    └── system/           # NEW: System-level config
        ├── constants.py  # ← FROM kb_catalog/constants/
        └── registry.json # ← FROM kb_catalog/registry/

Option B: Keep as definitions/catalog/

definitions/
├── config/              # Operational config
└── catalog/            # System inventory
    ├── constants/       # Enum-like data
    ├── registry/        # Component registry
    └── manifests/       # Auto-generated inventories

Recommendation: Option B if catalog is auto-generated inventory.
Rationale: Catalogs are metadata ABOUT the system, not config FOR the system.

2. `storage/examples/` - Code or Data?

Current Reality: It's CODE (retriever.py, matcher.py)

Decision: Keep in weave/storage/examples/ as a code module

Clarify with README:

# weave/storage/examples/README.md
 
This is a **Python module** for runtime example retrieval, NOT a data directory.
 
Training examples live in: `data_fabric/definitions/examples/`

3. Generated Schemas - Where?

Current: schemas/generated/
Proposed: definitions/schemas/generated/

Rationale: Generated FROM canonical, so still "definitions"

Alternative View: Move to views/schemas/ since they're derived

Recommendation: Keep in definitions/schemas/generated/

These are source code (imported by apps)
They're checked into git (not gitignored)
They're versioned (breaking changes matter)

4. Pipeline Stage Configs - Where?

Question: Should each pipeline stage have its own config?

Current: Global config in output-styles/config/

Recommendation: Centralized in definitions/config/

definitions/
└── config/
    ├── business/          # Domain config (pricing, scoring)
    ├── pipeline/          # Pipeline-wide settings
    │   ├── onboarding_stages.json
    │   └── validation_rules.json
    └── sports/            # Sport-specific config

Rationale:

✅ Single source of truth
✅ Easier to version
✅ Avoids duplication across stages
✅ Pipeline stages READ config, don't OWN it

📝 Final Recommendation Summary

✅ Organization Strategy: HYBRID

Level 1 (Lifecycle): definitions/ → weave/ → views/
Level 2 (Within definitions/): Domain-sliced (pricing, scoring, sports)
Level 3 (Within views/): Scenario-sliced (onboarding, analytics)

✅ Directory Structure

data_fabric/
├── definitions/         # Lifecycle Stage 1: Source of truth
│   ├── schemas/         # Domain-organized
│   ├── config/          # Domain-organized (business, sports, pipeline)
│   ├── templates/       # Scenario-organized (prompts, contracts)
│   └── examples/        # Scenario-organized (training data)
│
├── weave/              # Lifecycle Stage 2: Runtime operations
│   ├── knowledge/       # Technical slice (AI)
│   ├── storage/         # Technical slice (persistence)
│   ├── prompts/         # Technical slice (generation)
│   ├── generators/      # Technical slice (transformation)
│   └── validators/      # Technical slice (validation)
│
├── views/              # Lifecycle Stage 3: Generated outputs
│   ├── onboarding/      # Scenario-organized
│   ├── analytics/       # Scenario-organized
│   ├── contracts/       # Scenario-organized
│   └── uploads/         # Scenario-organized
│
├── docs/               # Documentation (lifecycle-agnostic)
├── scripts/            # Utilities (lifecycle-agnostic)
└── tests/              # Testing (lifecycle-agnostic)

✅ Migration Priority

Week 1: Create structure, copy (don't move) files
Week 2: Update imports, test thoroughly
Week 3: Delete old structure, update docs

✅ Why This Works

Concern	Solution
"Where does X go?"	Lifecycle first → domain/scenario second
"Too many directories"	Only 3 top-level (definitions, weave, views)
"Hard to navigate"	IDE search + clear README in each
"Breaking changes"	Copy-then-migrate strategy
"Team confusion"	Visual diagram + onboarding doc

🎓 Teaching the System

Create: `data_fabric/README.md`

# Data Fabric Architecture
 
This directory uses a **hybrid lifecycle + domain organization**.
 
## 🗂️ Top-Level Structure
 
- `definitions/` - **Source of truth** (git-tracked, immutable)
- `weave/` - **Operational code** (Python modules, runtime logic)
- `views/` - **Generated outputs** (.gitignored, materialized views)
 
## 🧭 Finding What You Need
 
**Looking for business rules?** → `definitions/config/business/`
**Looking for AI prompts?** → `definitions/templates/prompts/`
**Looking for schemas?** → `definitions/schemas/domain/`
**Looking for runtime code?** → `weave/{knowledge,storage,prompts}/`
**Looking for pipeline outputs?** → `views/onboarding/`
 
## 📚 Learn More
 
- [Lifecycle Guide](docs/LIFECYCLE_GUIDE.md)
- [Domain Guide](docs/DOMAIN_GUIDE.md)
- [Scenario Guide](docs/SCENARIO_GUIDE.md)

Bottom Line: Use HYBRID organization with lifecycle at the top level, domain slicing for config/schemas, and scenario slicing for workflows/examples.

✅ Sports Betting Readiness Analysis System - COMPLETE 🎯 Comprehensive Knowledge Organization Plan

🎯 Complete Organization Strategy Analysis

📋 Current State Assessment

What You Have Now

🎨 Organization Philosophies Explained

1️⃣ Lifecycle Stage Organization

2️⃣ Slice/Domain Organization

3️⃣ Scenario/Workflow Organization

4️⃣ Hybrid Organization ⭐ RECOMMENDED

🎯 Decision Matrix for YOUR System

Your System Characteristics

🏗️ Recommended Structure (Complete)

Top-Level: Lifecycle Stages

Level 1: `definitions/` - SOURCE OF TRUTH

Level 2: `weave/` - OPERATIONAL RUNTIME

Level 3: `views/` - MATERIALIZED OUTPUTS

📊 Comparison: Current vs Recommended

Current Structure Issues

Recommended Structure Benefits

🔄 Migration Strategy

Phase 1: Non-Breaking Additions (Week 1)

Phase 2: Copy (Don't Move) Critical Files (Week 1)

Phase 3: Update Import Paths (Week 2)

Phase 4: Test & Validate (Week 2)

Phase 5: Delete Old Structure (Week 3)

🎯 Special Considerations

1. `kb_catalog/` - Where Does It Go?

2. `storage/examples/` - Code or Data?

3. Generated Schemas - Where?

4. Pipeline Stage Configs - Where?

📝 Final Recommendation Summary

✅ Organization Strategy: HYBRID

✅ Directory Structure

✅ Migration Priority

✅ Why This Works

🎓 Teaching the System

Create: `data_fabric/README.md`

Platform

Documentation

Community

Support

🎯 Complete Organization Strategy Analysis

📋 Current State Assessment

What You Have Now

🎨 Organization Philosophies Explained

1️⃣ Lifecycle Stage Organization

2️⃣ Slice/Domain Organization

3️⃣ Scenario/Workflow Organization

4️⃣ Hybrid Organization ⭐ RECOMMENDED

🎯 Decision Matrix for YOUR System

Your System Characteristics

🏗️ Recommended Structure (Complete)

Top-Level: Lifecycle Stages

Level 1: definitions/ - SOURCE OF TRUTH

Level 2: weave/ - OPERATIONAL RUNTIME

Level 3: views/ - MATERIALIZED OUTPUTS

📊 Comparison: Current vs Recommended

Current Structure Issues

Recommended Structure Benefits

🔄 Migration Strategy

Phase 1: Non-Breaking Additions (Week 1)

Phase 2: Copy (Don't Move) Critical Files (Week 1)

Phase 3: Update Import Paths (Week 2)

Phase 4: Test & Validate (Week 2)

Phase 5: Delete Old Structure (Week 3)

🎯 Special Considerations

1. kb_catalog/ - Where Does It Go?

2. storage/examples/ - Code or Data?

3. Generated Schemas - Where?

4. Pipeline Stage Configs - Where?

📝 Final Recommendation Summary

✅ Organization Strategy: HYBRID

✅ Directory Structure

✅ Migration Priority

✅ Why This Works

🎓 Teaching the System

Create: data_fabric/README.md

Platform

Documentation

Community

Support

Level 1: `definitions/` - SOURCE OF TRUTH

Level 2: `weave/` - OPERATIONAL RUNTIME

Level 3: `views/` - MATERIALIZED OUTPUTS

1. `kb_catalog/` - Where Does It Go?

2. `storage/examples/` - Code or Data?

Create: `data_fabric/README.md`