Terminal.skills
Skills/superset
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superset

Apache Superset is an open-source data exploration and visualization platform. Learn Docker deployment, database connections, chart creation, dashboard building, SQL Lab usage, and programmatic access via the REST API.

#superset#apache-superset#business-intelligence#dashboards#docker
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed superset v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Analyze the sales data in revenue.csv and identify trends"
  • "Create a visualization comparing Q1 vs Q2 performance metrics"

Information

Version
1.0.0
Author
terminal-skills
Category
Data & AI
License
Apache-2.0

Documentation

Apache Superset is a modern BI platform that supports rich visualizations, SQL Lab for ad-hoc queries, and a no-code chart builder. It connects to most SQL databases.

Installation

bash
# Docker Compose (official method)
git clone https://github.com/apache/superset.git
cd superset
docker compose -f docker-compose-non-dev.yml up -d

# Access at http://localhost:8088 (admin/admin)
yaml
# docker-compose.yml: Minimal Superset with PostgreSQL
services:
  superset:
    image: apache/superset:3.1.0
    ports:
      - "8088:8088"
    environment:
      SUPERSET_SECRET_KEY: your-secret-key-change-me
      DATABASE_URL: postgresql+psycopg2://superset:superset@postgres/superset
    depends_on:
      - postgres
      - redis
    volumes:
      - ./superset_config.py:/app/pythonpath/superset_config.py

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

  redis:
    image: redis:7

volumes:
  pg-data:
python
# superset_config.py: Basic Superset configuration
SECRET_KEY = 'your-secret-key-change-me'
SQLALCHEMY_DATABASE_URI = 'postgresql+psycopg2://superset:superset@postgres/superset'

# Feature flags
FEATURE_FLAGS = {
    'ENABLE_TEMPLATE_PROCESSING': True,
    'DASHBOARD_NATIVE_FILTERS': True,
    'EMBEDDED_SUPERSET': True,
}

# Cache config
CACHE_CONFIG = {
    'CACHE_TYPE': 'RedisCache',
    'CACHE_DEFAULT_TIMEOUT': 300,
    'CACHE_KEY_PREFIX': 'superset_',
    'CACHE_REDIS_URL': 'redis://redis:6379/0',
}

Initial Setup

bash
# setup.sh: Initialize Superset after first deploy
# Create admin user
docker exec -it superset superset fab create-admin \
  --username admin \
  --firstname Admin \
  --lastname User \
  --email admin@example.com \
  --password admin

# Initialize the database
docker exec -it superset superset db upgrade

# Load example dashboards (optional)
docker exec -it superset superset load_examples

# Initialize roles and permissions
docker exec -it superset superset init

Connect a Database

bash
# add-database.sh: Add a data source via API
TOKEN=$(curl -s -X POST http://localhost:8088/api/v1/security/login \
  -H "Content-Type: application/json" \
  -d '{"username": "admin", "password": "admin", "provider": "db"}' \
  | jq -r '.access_token')

curl -X POST http://localhost:8088/api/v1/database/ \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "database_name": "Production Analytics",
    "engine": "postgresql",
    "sqlalchemy_uri": "postgresql://readonly:pass@prod-db:5432/analytics",
    "expose_in_sqllab": true,
    "allow_ctas": false,
    "allow_cvas": false
  }'

SQL Lab

SQL Lab is Superset's interactive SQL editor:

1. Navigate to SQL Lab → SQL Editor
2. Select your database and schema
3. Write and execute queries
4. Save results as a dataset for chart building
5. Use Jinja templates for dynamic queries:
   SELECT * FROM orders
   WHERE created_at >= '{{ from_dttm }}' AND created_at < '{{ to_dttm }}'

Create Charts via API

python
# create-chart.py: Programmatically create a chart
import requests

BASE = 'http://localhost:8088/api/v1'
TOKEN = 'your-access-token'
headers = {'Authorization': f'Bearer {TOKEN}', 'Content-Type': 'application/json'}

# Create a chart (slice)
chart = requests.post(f'{BASE}/chart/', headers=headers, json={
    'slice_name': 'Monthly Revenue',
    'viz_type': 'echarts_timeseries_line',
    'datasource_id': 1,
    'datasource_type': 'table',
    'params': '{"metrics": ["sum__revenue"], "groupby": ["category"], "time_range": "Last year"}',
}).json()

print(f"Chart created: {chart['id']}")

Create Dashboard via API

python
# create-dashboard.py: Create a dashboard and add charts
dashboard = requests.post(f'{BASE}/dashboard/', headers=headers, json={
    'dashboard_title': 'Revenue Analytics',
    'published': True,
    'slug': 'revenue-analytics',
}).json()

dashboard_id = dashboard['result']['id']
print(f"Dashboard: http://localhost:8088/superset/dashboard/{dashboard_id}/")

Export and Import

bash
# export-import.sh: Export dashboards for version control
# Export dashboard as ZIP
curl -o dashboard.zip \
  -H "Authorization: Bearer $TOKEN" \
  "http://localhost:8088/api/v1/dashboard/export/?q=[1]"

# Import dashboard
curl -X POST "http://localhost:8088/api/v1/dashboard/import/" \
  -H "Authorization: Bearer $TOKEN" \
  -F "formData=@dashboard.zip" \
  -F "overwrite=true"

Role-Based Access

Superset RBAC:
- Admin: Full access to all features
- Alpha: Access to all data sources, can create charts/dashboards
- Gamma: Access only to granted datasets and dashboards
- sql_lab: Permission to use SQL Lab

Custom roles: Settings → List Roles → Add new role with specific permissions
Row-level security: Settings → Row Level Security → Add filter per role