How jithendra-data/datrixon works
Datrixon turns synthetic ERP records into controlled analytics and a static investigation dashboard. The main workflow is generation or reuse of raw CSVs, mandatory source checks, normalized staging, SQLite loading, SQL and Python analytics, independent reconciliation, contract validation, and approved JSON publication; the browser then presents financial views and investigation queues. The README documents major marts and operational analyses beyond the sampled excerpts. Exact wiring for unsampled analytics and browser modules is limited to documented behavior.
Main entry points: run_pipeline.py Pipeline orchestrator · app.js Dashboard application · export_dashboard_data.py Dashboard exporter · clean_data.py CSV normalization
Components 22
Data pipeline
- Pipeline orchestrator
etl/run_pipeline.py - Synthetic ERP generator
python/generators/generate_all.py - Master data generator
python/generators/generate_master_data.py - CSV normalization
etl/clean_data.py - SQLite warehouse loader
etl/warehouse.py - Dashboard exporter
etl/export_dashboard_data.py
Analytics and controls
- Source quality checks
validation/run_data_quality_checks.py - SQL marts and KPIs
sql/06_marts/sales_performance.sql - Python analytics
etl/build_analytics.py - Operational analytics
etl/operational.py - Independent reconciliation
validation/reconciliation.py - Publication controls
validation/publication.py
Decision product
- Static dashboard
web/index.html - Dashboard application
web/js/app.js - Executive views
web/js/executive.js - Metric views
web/js/metrics.js - Investigation queues
web/js/decisions.js - Reliability views
web/js/reliability.js
Published data
- Raw ERP CSVs
data/raw - SQLite reference warehouse
sql/sqlite/warehouse.sql - Approved dashboard JSON
web/data/dashboard.json
Other components
- Distribution leadership
Connections 29
- Distribution leadership → Static dashboard: investigates
- Pipeline orchestrator → Master data generator: creates masters
- Pipeline orchestrator → Synthetic ERP generator: builds transactions
- Master data generator → Synthetic ERP generator: supplies records
- Pipeline orchestrator → Raw ERP CSVs: writes CSVs
- Pipeline orchestrator → Source quality checks: validates sources
- Source quality checks → Raw ERP CSVs: checks records
- Pipeline orchestrator → CSV normalization: normalizes CSVs
- Raw ERP CSVs → CSV normalization: reads sources
- Pipeline orchestrator → SQLite warehouse loader: loads staging
- CSV normalization → SQLite warehouse loader: provides CSVs
- SQLite warehouse loader → SQLite reference warehouse: populates facts
- Pipeline orchestrator → Python analytics: builds aggregates
- Pipeline orchestrator → Operational analytics: builds operations
- Pipeline orchestrator → Dashboard exporter: exports analytics
- SQL marts and KPIs → Dashboard exporter: provides marts
- Python analytics → Dashboard exporter: provides aggregates
- Operational analytics → Dashboard exporter: provides findings
- Pipeline orchestrator → Independent reconciliation: reconciles outputs
- Pipeline orchestrator → Publication controls: validates contract
- Independent reconciliation → Publication controls: supplies results
- Dashboard exporter → Approved dashboard JSON: serializes candidate
- Publication controls → Approved dashboard JSON: approves replacement
- Static dashboard → Dashboard application: hosts application
- Dashboard application → Approved dashboard JSON: fetches JSON
- Dashboard application → Executive views: renders overview
- Dashboard application → Metric views: renders metrics
- Dashboard application → Investigation queues: renders queues
- Dashboard application → Reliability views: renders controls