# 🏀 NBA Analytics — Streamlit & dbt Data Warehouse

[![NBA Data Pipeline](https://git.turbo-data.com/nprasad2077/Streamlit_NBA/actions/workflows/pipeline.yaml/badge.svg?branch=main)](https://git.turbo-data.com/nprasad2077/Streamlit_NBA/actions?workflow=pipeline.yaml)
[![Live Dashboard](https://img.shields.io/badge/Live%20Dashboard-streamlit.turbo--data.com-FF6B00?logo=streamlit)](https://streamlit.turbo-data.com)

A modern NBA analytics data platform powered by **dbt**, **DuckDB**, and **Streamlit**. Transforms raw basketball event data into a dimensional star schema (32,000+ games, 480,000+ shots from 2002 to 2026), persisting artifacts to **Cloudflare R2 Object Storage** with automated **Gitea Actions** CI/CD and self-hosted **Coolify** deployment.

---

## 🏛️ Architecture

```mermaid
flowchart LR
    PG[(Postgres Source)] -->|scripts/extract.sql| LOCAL_DB[(DuckDB 223 MB)]
    LOCAL_DB -->|dbt build| MARTS[Marts Star Schema]
    MARTS -->|db_storage.py upload| R2[(Cloudflare R2 Storage)]
    R2 -->|Smart Sync| COOLIFY[Coolify: streamlit.turbo-data.com]
    R2 -->|run_app.sh| LOCAL_APP[Local Streamlit Dashboard]
```

### Medallion Data Modeling

| Layer | Models | Description |
|---|---|---|
| **Staging** | `stg_games`, `stg_line_scores`, `stg_player_game_basic_stats`, `stg_player_game_adv_stats`, `stg_player_shot_charts`, `stg_team_game_basic_stats`, `stg_team_game_adv_stats`, `stg_season_thresholds`, `stg_team_season_thresholds` | Clean raw source data, compute seasonal percentile thresholds, type casting |
| **Intermediate** | `int_games_enriched`, `int_player_performance`, `int_team_performance`, `int_player_shots_enriched` | Entity resolution across team history maps, shot chart & free throw unification |
| **Dimensions** | `dim_teams`, `dim_players`, `dim_seasons`, `dim_dates`, `dim_arenas`, `dim_shot_zones`, `dim_player_game_archetypes` | Conformed entity dimensions |
| **Facts** | `fct_game_results`, `fct_team_game_stats`, `fct_player_game_stats`, `fct_quarter_scoring`, `fct_player_shots`, `fct_player_game_shooting` | High-performance analytical fact tables with surrogate keys |

---

## 🚀 Quickstart & Local Setup

### 1. Prerequisites
- [uv](https://docs.astral.sh/uv/) (Python package manager)
- [DuckDB CLI](https://duckdb.org/docs/installation/) (v1.3.0+)

### 2. Clone and Install Dependencies
```bash
git clone https://git.turbo-data.com/nprasad2077/Streamlit_NBA.git
cd Streamlit_NBA

# Install Python 3.12 and dependencies into .venv
uv sync
```

### 3. Configure Environment Variables
Copy the template and fill in your credentials:
```bash
cp .env.example .env
```

```ini
# Postgres Source Database (for ETL extraction)
POSTGRES_URL=postgresql://user:password@host:5432/nba

# Cloudflare R2 / S3 Object Storage
S3_ENDPOINT_URL=https://<ACCOUNT_ID>.r2.cloudflarestorage.com
S3_BUCKET_NAME=dbt-duckdb
S3_ACCESS_KEY_ID=your_r2_access_key
S3_SECRET_ACCESS_KEY=your_r2_secret_key
S3_REGION=auto
S3_DB_KEY=dbt_nba.duckdb
```

### 4. Launch the Streamlit Dashboard
```bash
./scripts/run_app.sh
```
*The launcher automatically verifies and downloads the latest DuckDB database from Cloudflare R2 if it is not already present locally.*

---

## 🗄️ Database Storage Management

Manage your analytical DuckDB file via the custom storage utility:

```bash
# Check remote R2 object vs local file status
uv run python scripts/db_storage.py status

# Download latest database (skips if ETag/size already matches)
uv run python scripts/db_storage.py download

# Force re-download from Cloudflare R2
uv run python scripts/db_storage.py download --force

# Upload local database to Cloudflare R2
uv run python scripts/db_storage.py upload
```

---

## ⚙️ Running the ETL Pipeline

To execute the complete ETL process locally (Extract from Postgres $\rightarrow$ Run dbt transformations $\rightarrow$ Upload artifact to Cloudflare R2):

```bash
./scripts/pipeline.sh
```

---

## 🛠️ dbt Development Commands

Run dbt models and tests directly using `uv`:

```bash
# Install dbt packages (dbt_utils)
uv run dbt deps

# Parse and validate project manifest
uv run dbt parse

# Build all models, seeds, and tests
uv run dbt build

# Build specific layer by tag
uv run dbt build --select "tag:staging"
uv run dbt build --select "tag:intermediate"
uv run dbt build --select "tag:marts"

# Run tests only
uv run dbt test
```

---

## 🚢 CI/CD & Deployment

- **Gitea Actions (`.gitea/workflows/pipeline.yaml`)**: Runs scheduled daily ETL builds (06:00 UTC) on a custom `ubuntu-latest` runner and updates Cloudflare R2.
- **Coolify Self-Hosting ([streamlit.turbo-data.com](https://streamlit.turbo-data.com))**: Production deployment built via Dockerfile with automated Let's Encrypt SSL and persistent storage caching.