chidozinyakatira/african-econ-pipeline

# african-econ-pipeline # 🌍 African Economic Intelligence Pipeline A production-style data engineering pipeline that ingests World Bank economic indicators for 48 African countries, transforms them through a layered data model, orchestrates runs with Apache Airflow, and visualises insights in Power BI. --- ## 📐 Architecture ``` World Bank API │ ▼ Python (Google Colab) - Fetches 8 indicators × 48 countries × 24 years - Cleans, parses, and loads to BigQuery │ ▼ Google BigQuery african_economics_raw.world_bank_indicators │ ▼ dbt Cloud ├── staging: stg_world_bank_indicators (view) ├── marts: dim_countries (table) └── marts: fct_economic_indicators (table) │ ▼ Apache Airflow (Docker) - Schedules weekly pipeline runs - DAG: ingest → dbt staging → dbt marts │ ▼ Power BI - Continental Overview (Page 1) - Country Deepdive (Page 2) ``` --- ## 📊 Dashboard The Power BI dashboard has two pages: **Page 1 — Continental Overview** - KPI cards: 48 countries tracked, average GDP growth, largest economy, fastest growing economy - Africa map coloured by GDP size - Top 10 economies bar chart - Year slicer (2000–2023) **Page 2 — Country Deepdive** - Country slicer — select any of 48 countries - KPI cards: GDP per capita, inflation rate, unemployment rate, trade % of GDP - GDP growth rate over time (line chart) - FDI net inflows over time (line chart) - Inflation over time (line chart) - Top 10 economies by GDP (bar chart) Dashboard files are in the `/dashboard` folder (PDF and PowerPoint). --- ## 🗂️ Repository Structure ``` african-econ-pipeline/ ├── ingestion/ │ └── african_econ_pipeline_m1.py # World Bank API → BigQuery ├── dbt/ │ └── models/ │ ├── staging/ │ │ ├── sources.yml │ │ └── stg_world_bank_indicators.sql │ └── marts/ │ ├── dim_countries.sql │ └── fct_economic_indicators.sql ├── dags/ │ └── african_econ_dag.py # Airflow DAG ├── dashboard/ │ ├── African_Economic_Intelligence.pdf │ └── Afri …