# Apache Superset — Open Source Data Visualization & Exploration

> Apache Superset is a modern data exploration and visualization platform. Connect any SQL database, build interactive dashboards with 40+ chart types, no coding required.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

## Quick Use

```bash
docker run -d --name superset -p 8088:8088 
  -e SUPERSET_SECRET_KEY=your-secret-key 
  apache/superset:latest
```

Then initialize:
```bash
docker exec -it superset superset fab create-admin 
  --username admin --firstname Admin --lastname User 
  --email admin@example.com --password admin
docker exec -it superset superset db upgrade
docker exec -it superset superset init
```

Open `http://localhost:8088` — login and connect your database.

## Intro

**Apache Superset** is a modern, enterprise-ready data exploration and visualization platform. It provides an intuitive interface for exploring data, building interactive dashboards with 40+ chart types, and sharing insights — all through a web browser without writing code. Power users can write SQL for advanced analysis.

With 72.3K+ GitHub stars and Apache-2.0 license, Superset is the most popular open-source BI platform under the Apache Software Foundation umbrella, used by Airbnb, Dropbox, Lyft, Netflix, and thousands of organizations worldwide.

## What Superset Does

- **40+ Chart Types**: Bar, line, area, scatter, pie, heatmap, map, treemap, sunburst, pivot table, and more
- **SQL Editor**: Full SQL IDE with autocomplete, saved queries, and result visualization
- **Dashboard Builder**: Drag-and-drop dashboard creation with cross-filtering and drill-down
- **Data Exploration**: No-code exploration with visual query builder for slicing and dicing data
- **30+ Database Connectors**: PostgreSQL, MySQL, ClickHouse, BigQuery, Snowflake, Redshift, Trino, Druid, etc.
- **Access Control**: Role-based access with row-level security and dataset permissions
- **Caching**: Query result caching with configurable TTL for dashboard performance
- **Alerts & Reports**: Scheduled reports and threshold-based alerts via email/Slack
- **Embedding**: Embed dashboards and charts into external applications

## Architecture

```
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Browser     │────▶│  Superset    │────▶│  Your Data   │
│  Dashboards  │     │  Server      │     │  PostgreSQL  │
└──────────────┘     │  (Python/    │     │  ClickHouse  │
                     │   Flask)     │     │  BigQuery    │
                     └──────┬───────┘     │  Snowflake   │
                            │             │  30+ more    │
                  ┌─────────┼─────────┐   └──────────────┘
                  │         │         │
           ┌──────┴──┐ ┌───┴───┐ ┌───┴───┐
           │  Redis  │ │Celery │ │Metadata│
           │ (Cache) │ │(Async)│ │  DB    │
           └─────────┘ └───────┘ └────────┘
```

## Self-Hosting

### Docker Compose

```yaml
services:
  superset:
    image: apache/superset:latest
    ports:
      - "8088:8088"
    environment:
      SUPERSET_SECRET_KEY: your-long-secret-key
      DATABASE_URL: postgresql+psycopg2://superset:superset@db:5432/superset
      REDIS_URL: redis://redis:6379/0
    depends_on:
      - db
      - redis

  db:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: superset
      POSTGRES_PASSWORD: superset
      POSTGRES_DB: superset
    volumes:
      - pg-data:/var/lib/postgresql/data

  redis:
    image: redis:7-alpine

volumes:
  pg-data:
```

## Key Features

### No-Code Exploration

```
1. Select dataset (table/view)
2. Choose chart type (bar, line, etc.)
3. Drag metrics (SUM, COUNT, AVG)
4. Add dimensions (group by)
5. Apply filters
6. Customize colors, labels, formatting
7. Save to dashboard
```

### SQL Lab (SQL IDE)

```sql
-- Full SQL editor with schema browser
SELECT
  DATE_TRUNC('month', order_date) as month,
  product_category,
  SUM(revenue) as total_revenue,
  COUNT(DISTINCT customer_id) as unique_customers
FROM orders
WHERE order_date >= '2024-01-01'
GROUP BY 1, 2
ORDER BY 1, 3 DESC

-- Results can be instantly visualized as any chart type
-- Save queries for reuse
-- Share with team members
```

### Dashboard Features

- **Cross-filtering**: Click on one chart to filter all others
- **Filter bar**: Global filters affecting all charts
- **Drill-down**: Click data points to explore deeper
- **Annotations**: Mark events on time-series charts
- **CSS customization**: Custom styling per dashboard
- **Auto-refresh**: Configurable refresh intervals

### Jinja Templating

```sql
-- Dynamic queries with user context
SELECT * FROM orders
WHERE region = '{{ current_user().region }}'
  AND order_date >= '{{ from_dttm }}'
  AND order_date <= '{{ to_dttm }}'

{% if filter_values('status') %}
  AND status IN ({{ "'" + "', '".join(filter_values('status')) + "'" }})
{% endif %}
```

## Superset vs Alternatives

| Feature | Superset | Metabase | Grafana | Looker | Tableau |
|---------|----------|----------|---------|--------|---------|
| Open Source | Yes (Apache-2.0) | Yes (AGPL) | Yes (AGPL) | No | No |
| Chart types | 40+ | 15+ | 20+ | LookML | Extensive |
| SQL IDE | Yes | Basic | No | No | No |
| No-code explore | Yes | Yes | Limited | LookML | Yes |
| Databases | 30+ | 20+ | 100+ (metrics) | BigQuery focus | Any |
| Row-level security | Yes | Enterprise | No | Yes | Yes |
| Best for | Data teams | Business users | DevOps metrics | Enterprises | Enterprises |

## FAQ

**Q: Superset or Metabase — which should I pick?**
A: Metabase is friendlier for non-technical users (its visual query builder is easier). Superset is better for data teams (more powerful SQL Lab, more chart types, better performance). If your team is comfortable with SQL, go with Superset.

**Q: Can it handle big data?**
A: Superset doesn't store data itself — performance depends on the underlying database. Paired with an OLAP engine like ClickHouse, Druid, or Trino, it can query billions of rows. The Redis cache layer also significantly speeds up dashboard loads.

**Q: Is self-hosting difficult?**
A: Docker deployment is relatively simple. Production requires Redis (cache), Celery (async tasks), and PostgreSQL (metadata). The Apache project provides a detailed production deployment guide and Helm chart.

## Sources & Credits

- GitHub: [apache/superset](https://github.com/apache/superset) — 72.3K+ ⭐ | Apache-2.0
- Website: [superset.apache.org](https://superset.apache.org)

---
Source: https://tokrepo.com/en/workflows/apache-superset-open-source-data-visualization-exploration-58b28055
Author: Apache Software Foundation