Library Book Inventory & Issue Tracking System (BCA + Analytics Modernisation)

This repository is a portfolio-safe, end-to-end project that shows progression from a BCA baseline system (v1) to an analytics modernisation layer (v2).

• v1_bca_basic_system/ — CRUD-style workflow: Books → Members → Issue/Return → Fines → Reports/Exports
• v2_analytics_modernisation/ — synthetic data → ETL → Data Quality gate → curated outputs → warehouse model → dashboard exports + monitoring

Portfolio safety: this repo contains synthetic/demo data only (no real member identities, no real borrowing history).
Last updated: 2026-02-03

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What you can review quickly (top evidence)

v1 UI screenshots (baseline app)

Location: v1_bca_basic_system/ui/screenshots/

Preview:

v2 Dashboard exports (Power BI-style PNG)

Location: v2_analytics_modernisation/bi_powerbi/exports/

Preview:

Mermaid (architecture & KPI trees)

Mermaid sources: docs/mermaid/
Start with:

• docs/mermaid/architecture_flow.mmd
• docs/mermaid/kpi_tree.mmd
• docs/mermaid/warehouse_star_schema.mmd

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Architecture at a glance (v1 + v2)

%%{init: {"theme":"base","flowchart":{"curve":"basis","nodeSpacing":55,"rankSpacing":80},"themeVariables":{"fontFamily":"Inter, Segoe UI, Arial","fontSize":"14px"}}}%%
flowchart LR
  subgraph V1["v1 Baseline System (BCA)"]
    UI[UI Forms] --> APP[Application Logic]
    APP --> DB[(SQLite OLTP)]
    APP --> EXP[Exports: CSV]
  end

  subgraph V2["v2 Analytics Modernisation"]
    RAW[Raw Synthetic Data] --> ETL[ETL Pipeline]
    ETL --> STG[Staged Outputs]
    STG --> DQ[Data Quality Checks]
    DQ --> CUR[Curated Outputs]
    CUR --> WH[(Warehouse-ready Star Schema)]
    WH --> BI[Dashboard Exports]
    WH --> MON[Monitoring + Run Register]
  end

  DB --> RAW
  EXP --> RAW

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How to run locally (portfolio demo)

Prerequisites

• Python 3.10+ recommended

v2 quick run

cd v2_analytics_modernisation
pip install -r requirements.txt

# 1) generate raw synthetic data
python scripts/make_sample_data.py

# 2) run the ETL to produce staged + curated outputs
python etl/run_etl.py

# 3) run data quality checks (JSON + HTML report)
python dq_data_quality/run_checks.py

# 4) generate dashboard exports (PNG)
python scripts/generate_bi_exports.py

Outputs you should see:

• v2_analytics_modernisation/data/raw/
• v2_analytics_modernisation/data_output/staged/
• v2_analytics_modernisation/data_output/curated/
• v2_analytics_modernisation/reports/dq_summary.json
• v2_analytics_modernisation/reports/dq_report.html
• v2_analytics_modernisation/bi_powerbi/exports/*.png

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Governance, QA and evidence packs

• Governance logs: docs/logs/ (progress log, run register, issues log, hours)
• QA / reproducibility: docs/quality/ (test strategy, DQ strategy, reproducibility checklist)
• Evidence index: EVIDENCE_INDEX.md (where to find the main proof artefacts)
• Evidence images: docs/evidence/ (v1 UI, v2 pipeline, v2 BI, diagrams)
• Sample runtime logs: logs/sample_logs_only/ (safe, no secrets)

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Repo map

• v1 baseline: v1_bca_basic_system/
• v2 analytics: v2_analytics_modernisation/
• Documentation packs: docs/
• Safe runtime logs: logs/

For a full file list, see REFERENCE_FILE_STRUCTURE.md.