I built this to model an executive/operator command center that turns platform telemetry into scorecards, health views, and reviewable operating signals.
The key challenge I wanted to capture was the part that usually gets hidden in simple demos: how data, signals, decisions, constraints, evidence, and operating risk move through a system that someone else could inspect and run locally.
I intentionally kept this version local and synthetic because the goal is to make the architecture and tradeoffs reviewable without external services, private data, paid APIs, or cloud setup.
Enterprise data platforms need a unified view of reliability, governance, data quality, AI readiness, semantic trust, and operating risk.
This matters because production teams do not only need outputs. They need evidence, ownership, repeatable validation, failure modes, and a path from local prototype to governed production system.
- platform observability
- scorecard design
- data governance
- executive reporting
- API/dashboard serving
- systems integration thinking
- production-style data pipeline design
- synthetic but realistic data modeling
- scorecard generation
- testable architecture
- honest limitation framing
Synthetic platform signals are generated, scored, aggregated into command-center outputs, and served through API/dashboard layers with documentation and CI validation.
The important pattern is that inputs are not just transformed into outputs. They are turned into scored, documented artifacts that can be reviewed by operators, analysts, engineers, and business stakeholders.
- Synthetic data keeps the repo safe to run and share publicly.
- Deterministic local logic makes validation repeatable without paid APIs.
- DuckDB or local artifacts provide warehouse-style inspection without cloud setup.
- FastAPI shows how the system could be served as a service layer.
- Streamlit gives reviewers a fast way to inspect the outputs visually.
- Scorecards make quality, risk, reliability, or readiness measurable.
- Tests and Ruff keep the repo from being only documentation.
- Docker/CI files show the intended deployment shape without claiming production readiness.
See docs/design-decisions.md for the detailed tradeoff record.
Latest validation run: 2026-06-02.
- Pipeline: passed
- Pytest: passed (61 tests)
- Ruff: passed
- Repository quality docs check: passed
- Detailed command output is recorded in docs/validation-log.md.
- platform health scorecards
- governance metrics
- reliability outputs
- AI-readiness views
- FastAPI endpoints
- Streamlit dashboard
Recruiter / hiring manager:
- Read this README first.
- Review docs/recruiter-summary.md if present.
- Check docs/validation-log.md.
- Use docs/repo-review-guide.md for the quickest path.
Senior engineer:
- Review the architecture docs.
- Inspect the
src/modules. - Inspect tests and generated scorecards.
- Read docs/design-decisions.md and docs/tradeoffs-and-simplifications.md.
Interview path:
- Run the pipeline command from the validation log.
- Launch the dashboard or API if this repo includes them.
- Explain one design decision and one simplification honestly.
- Synthetic data only.
- Local prototype rather than deployed production system.
- Deterministic rules or simulations where a production system may use live models, streaming data, or enterprise integrations.
- No real sensitive data is used.
- No authentication, RBAC, secrets management, or production security boundary unless explicitly stated elsewhere in the repo.
- External systems are simulated instead of connected live.
- connect real platform telemetry
- integrate catalog/lineage tools
- deploy warehouse/lakehouse storage
- add alert routing
- add auth and ownership workflows
See docs/production-roadmap.md for the staged roadmap.
This project simulates an enterprise data and AI platform control plane.
A basic portfolio project asks: "Can this pipeline, model, or dashboard work?"
This project asks: "Can leadership see whether the entire data platform is healthy, governed, reliable, and ready for AI?"
Large enterprises often have separate tools for data quality, pipeline incidents, semantic metrics, AI governance, RAG evaluation, model monitoring, and lineage. The challenge is not lack of signals. The challenge is fragmented signals.
This project demonstrates platform-level data engineering: turning fragmented quality, governance, reliability, and AI signals into a unified enterprise command center.
Enterprise data platforms are fragmented. Data quality teams track failed checks, data engineering teams track DAG failures, MLOps teams track model drift, AI teams track RAG hallucination risk, governance teams track policy violations, and analytics teams track metric trust issues. Executives need one answer: is the data platform healthy enough for AI?
Build a production-style local command center that simulates platform signals from multiple enterprise data and AI systems, normalizes them into a common platform model, calculates unified platform health scores, correlates incidents, generates governance and AI readiness heatmaps, tracks data product health, summarizes SLA compliance, and exposes executive summaries through API and dashboard layers.
Is the enterprise data platform healthy, governed, reliable, and ready for AI consumption?
flowchart LR
A["Simulated Data / AI Systems"] --> B["Raw Platform Signals"]
B --> C["Signal Normalizer"]
C --> D["Data Product Catalog"]
C --> E["Health Scoring"]
C --> F["Governance + AI Readiness"]
C --> G["Incident Correlation"]
D --> H["Lineage Impact Graph"]
E --> I["Executive Scorecards"]
F --> I
G --> I
H --> I
I --> J["DuckDB Command Center Warehouse"]
J --> K["FastAPI"]
J --> L["Streamlit Dashboard"]
flowchart TD
A["Generate Catalog"] --> B["Generate Platform Signals"]
B --> C["Generate Incident Scenarios"]
C --> D["Normalize Signals"]
D --> E["Calculate Data Product Health"]
D --> F["Calculate Platform Health"]
D --> G["Calculate Governance Risk"]
D --> H["Calculate AI Readiness"]
E --> I["Correlate Cross-System Incidents"]
F --> J["Build Scorecards"]
G --> J
H --> J
I --> K["Lineage + Impact Graph"]
J --> L["API + Dashboard"]
- AI-ready Data Quality Command Center
- Enterprise RAG Evaluation Lab
- Payments Fraud Feature Store + MLOps
- Production Pipeline Reliability Lab
- Semantic Metrics Trust Layer
- AI Data Governance Gateway
platform_health_scorecard.json/csv: enterprise-level platform health and drivers.enterprise_risk_summary.json/csv: highest cross-system platform risks.data_product_health_report.json/csv: health score per data product.governance_heatmap.json/csv: governance risk by domain and product.ai_readiness_summary.json/csv: AI readiness by product and system.platform_sla_report.json/csv: SLA compliance summary.cross_platform_incident_report.json/csv: correlated incidents and downstream impact.system_dependency_graph.json: dependency graph across systems, products, consumers, and incidents.
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
python -m src.data_generation.generate_data_product_catalog
python -m src.data_generation.generate_platform_signals
python -m src.data_generation.generate_incident_scenarios
python -m src.pipeline.run_all
python -m pytest
python -m ruff check .
streamlit run src/dashboard/app.py
uvicorn src.api.main:app --reloadEndpoints include /health, /platform-summary, /data-products, /incidents, /governance-heatmap, /ai-readiness, /sla-report, /lineage-impact, /executive-summary, /scorecards, /simulate-incident, and /refresh-platform-health.
- Synthetic signals only
- Local DuckDB instead of an enterprise warehouse
- Deterministic scoring rules
- Simulated integrations instead of real tool APIs
- No cloud deployment
- No authentication
- No real alerting integration
- No OpenLineage, Datadog, Monte Carlo, Collibra, or Atlan integration yet
- Real ingestion from previous portfolio project outputs
- OpenLineage/Marquez integration
- DataHub/OpenMetadata integration
- Datadog/Prometheus/Grafana integration
- Monte Carlo/Bigeye-style observability
- PagerDuty/Slack alert routing
- Snowflake/Databricks deployment
- dbt metadata ingestion
- MLflow model registry ingestion
- Role-based access control
Enterprise data and AI platforms generate many health signals across quality, pipelines, RAG systems, ML models, semantic metrics, and AI governance. Those signals are fragmented, making platform risk hard to see.
Build a unified command center that normalizes signals, calculates health scores, correlates incidents, and provides executive visibility into reliability, governance, AI readiness, and downstream impact.
Created synthetic platform signals, common data product health models, scoring engines, incident correlation, SLA summaries, governance heatmaps, lineage impact graphs, API endpoints, dashboards, tests, Docker, and CI/CD.
Produced a reproducible portfolio project that demonstrates platform-level data engineering and AI infrastructure thinking.
V0.1: Working baseline.
I added a small readiness scorecard so the production roadmap is not just prose. The check reads config/future_enhancements.json, verifies the repo has the expected roadmap/review artifacts, and writes:
data/scorecards/future_enhancement_readiness.jsondata/scorecards/future_enhancement_readiness.csv
Run it with:
python scripts/generate_future_enhancement_scorecard.pyThis is a local planning signal, not a claim that the repository is production-ready.