Cognitive Data Architecture Framework

Reference implementation of the framework described in:

Sunil Kumar Mudusu, "Cognitive Data Architecture: Designing Self-Optimizing Frameworks for Scalable AI Systems" CIO, December 22, 2025 https://www.cio.com/article/4109911/cognitive-data-architecture-designing-self-optimizing-frameworks-for-scalable-ai-systems.html

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What this implements

The article proposes a cognitive data architecture where data pipelines are not static ETL processes but adaptive systems that learn from quality signals, apply semantic understanding, enforce governance rules, and self-optimize over time. This repository translates those concepts into a working Python framework.

Concretely:

• Secure ingestion — file validation, extension guards, automatic metadata capture
• Semantic layer — YAML-driven column-to-business-term mapping with entity and metric definitions
• Quality engine — null detection, duplicate checking, type validation, schema drift alerts
• Feedback loop — compares current pipeline run against prior runs to detect quality trends
• Self-optimization engine — recommends corrective actions based on quality, semantic, and governance signals
• Governance engine — declarative YAML policies: required metadata, sensitive field detection, domain allowlists, quality thresholds
• AI-readiness score — 0–100 score aggregating all pipeline stage results into a single dataset readiness signal
• Metadata store — SQLite-backed storage for all pipeline results, queryable history

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Repository structure

cognitive-data-architecture-framework/
├── src/cda/
│   ├── ingestion.py          # File loading, metadata capture, extension guard
│   ├── semantic_layer.py     # YAML semantic config loader and column mapper
│   ├── quality_engine.py     # Null, duplicate, type, and schema drift checks
│   ├── feedback_loop.py      # Run-over-run quality trend analysis
│   ├── optimizer.py          # Optimization action recommender
│   ├── governance.py         # YAML policy loader and governance rule evaluator
│   ├── ai_readiness_score.py # 0–100 AI-readiness scoring
│   ├── metadata_store.py     # SQLite-backed persistence layer
│   ├── config.py             # Configuration dataclasses
│   └── exceptions.py         # Typed exceptions per module
├── examples/
│   ├── sample_pipeline.py    # End-to-end pipeline demonstration
│   ├── sample_input.csv      # 20-row product sales dataset
│   ├── semantic_config.yaml  # Column semantic definitions
│   └── governance_policies.yaml
├── tests/                    # Pytest test suite (59 tests)
├── docs/                     # Architecture, article mapping, governance model
├── .github/workflows/ci.yml
├── Dockerfile
└── pyproject.toml

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Installation

git clone https://github.com/reachsunilmudusu-rgb/cognitive-data-architecture-framework.git
cd cognitive-data-architecture-framework

python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate

pip install -e ".[dev]"

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Quick start

from cda.ingestion import ingest_file
from cda.semantic_layer import load_semantic_config, apply_semantic_mapping
from cda.quality_engine import run_quality_checks
from cda.governance import load_governance_policies, check_governance
from cda.metadata_store import MetadataStore
from cda.feedback_loop import evaluate_feedback
from cda.optimizer import generate_optimizations
from cda.ai_readiness_score import calculate_ai_readiness

store    = MetadataStore()
ing      = ingest_file("examples/sample_input.csv", source_name="sales_q1")
sem_cfg  = load_semantic_config("examples/semantic_config.yaml")
semantic = apply_semantic_mapping(ing.columns, sem_cfg)
quality  = run_quality_checks(ing.records, dataset_id=ing.dataset_id,
                               source_path=ing.source_path,
                               numeric_columns=["unit_price", "quantity"])
policy   = load_governance_policies("examples/governance_policies.yaml")
gov      = check_governance(ing, quality, policy)
feedback = evaluate_feedback(quality, store)
store.save_quality_report(quality)
opts     = generate_optimizations(quality, semantic, gov, feedback)
score    = calculate_ai_readiness(quality, semantic, gov, feedback)

print(score.summary)

Run the full end-to-end pipeline:

python examples/sample_pipeline.py

Expected output:

==============================================================
Cognitive Data Architecture — Pipeline Run
==============================================================

[1] Ingesting data ...
    dataset_id   : <uuid>
    source       : sample_input.csv
    rows         : 20
    columns      : 8

[2] Applying semantic mapping ...
    mapped       : 8/8 columns
    coverage     : 100.0%
    required     : all present

[3] Running quality checks ...
    quality score: 100.0/100
    nulls        : 0.0%
    duplicates   : 0
    issues       : none

[4] Running governance checks ...
    [PASS] required_metadata: All required metadata present
    [PASS] sensitive_fields: No sensitive fields detected
    [PASS] allowed_domains: Source 'sample_sales_data' matches an allowed domain
    [PASS] minimum_quality_score: Quality score 100.0 meets minimum 60.0
    [PASS] max_null_percentage: Null rate 0.0% within limit of 20.0%
    [PASS] max_duplicate_percentage: Duplicate rate 0.0% within limit of 10.0%

[5] Evaluating feedback ...
    trend        : first_run
    score delta  : +0.0

[6] Storing metadata ...
    stored ingestion, quality, and governance results

[7] Generating optimization recommendations ...
    recommendation: APPROVE
    summary       : Dataset approved for AI use.

[8] Calculating AI-readiness score ...
    AI-readiness score 95/100 (grade A)

==============================================================
Verification Summary
==============================================================
  Dataset ID      : <uuid>
  Source          : sample_input.csv
  Rows ingested   : 20
  Quality Score   : 100.0/100
  Semantic        : COMPLETE
  Governance      : PASSED
  Feedback Trend  : FIRST_RUN
  AI-Readiness    : 95/100 (Grade A)
  Recommendation  : APPROVE
==============================================================

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Running tests

pytest -q

59 tests covering all modules with positive and negative cases. See docs/test_results.md for full output.

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Docker

docker build -t cda .
docker run --rm cda

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Verification checklist

• File accepted by ingestion (no IngestionError)
• All required columns present in semantic mapping
• QualityReport.quality_score >= 60
• Zero unexpected null rates above policy threshold
• GovernanceResult.passed == True
• No sensitive fields in column names
• FeedbackResult recorded (trend established)
• OptimizationResult.overall_recommendation in {approve, review}
• AIReadinessResult.score >= 75 (grade B or above)
• All results persisted to MetadataStore

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Documentation

Document	Description
Architecture	Module layout and data flow
Article Mapping	CIO article concept → implementation
Self-Optimization Design	How the optimizer works
Governance Model	Policy schema and decision logic
Test Results	Verified pytest output
Verification Checklist	Dataset readiness gates

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License

MIT. See LICENSE for full terms.

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Citation

Mudusu, S. K. (2025). Cognitive data architecture: Designing self-optimizing
frameworks for scalable AI systems. CIO. Published December 22, 2025.
https://www.cio.com/article/4109911/cognitive-data-architecture-designing-self-optimizing-frameworks-for-scalable-ai-systems.html