Skip to content

readme.md

Latest commit

 

History

History
200 lines (155 loc) · 6.5 KB

readme.md

File metadata and controls

200 lines (155 loc) · 6.5 KB

Connecting Apache Superset

In this article, we will walk through the process of connecting Apache Superset to the analytical database. To make it more illustrative, we will also build a dashboard that connects to the analytical database and monitors the real-time status of vehicles, all using Apache Superset.

Note: This guide is part of the Private Telematics Lakehouse documentation suite and covers connecting Apache Superset to your data warehouse. If you're still deciding which BI tool to use, refer to the Selecting BI tools overview.

Dashboard features

  • Display total number of objects
  • Visualize vehicle movement statuses (moving/stopped/parked)
  • Visualize connection statuses (active/idle/offline)
  • Detailed table with current status of all vehicles
  • Filtering by vehicle type, group, movement status, and connection status
  • Data and report export capabilities
  • Customizable notifications and alerts

Technical requirements

  • Docker and Docker Compose
  • Minimum 4 GB RAM (8 GB recommended)
  • 20 GB of free disk space
  • Linux/Windows with WSL2/macOS
  • Python 3.8+
  • Internet access for database connection

Installation and setup

1. Installation with Docker (recommended method)

  1. Install Docker and Docker Compose by following the official documentation:

  2. Download the official docker-compose file:

curl -fL https://raw.githubusercontent.com/apache/superset/master/docker-compose-non-dev.yml -o docker-compose.yml
  1. Start Superset:
docker-compose up -d
  1. Create an administrator:
docker-compose exec superset superset fab create-admin \
  --username admin \
  --firstname Superset \
  --lastname Admin \
  --email admin@superset.com \
  --password admin
  1. Initialize the database:
docker-compose exec superset superset db upgrade
  1. Load examples and initialize roles:
docker-compose exec superset superset init

2. Installation with pip (for development)

  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # for Linux/macOS
# or
.\venv\Scripts\activate  # for Windows
  1. Install Superset:
pip install apache-superset
  1. Initialize the database:
superset db upgrade
  1. Create an administrator:
export FLASK_APP=superset
superset fab create-admin
  1. Load examples and initialize roles:
superset init
  1. Start Superset:
superset run -p 8088 --with-threads --reload --debugger

Database connection

  1. Log in to Superset (default: http://localhost:8088)
  2. Navigate to Data → Databases
  3. Click "+" to add a new database
  4. Fill in the connection parameters:
    • Database: PostgreSQL
    • SQLAlchemy URI: postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}
    • Display Name: Analytics Database
    • Extra: {"engine_params": {"connect_args": {"sslmode": "require"}}}
  5. Click Test Connection to verify the connection
  6. Save the settings

Connection parameter reference

Lakehouse Parameter Apache Superset Setting Location Notes
Host DB_HOST in SQLAlchemy URI The database server address provided in your welcome email
Port DB_PORT in SQLAlchemy URI Default is 5432 for PostgreSQL
Database name DB_NAME in SQLAlchemy URI Your assigned database name
Username DB_USER in SQLAlchemy URI Your database username
Password DB_PASS in SQLAlchemy URI Your secure database password
SSL mode connect_args in Extra parameters Set to require in the Extra JSON configuration
Schema Dataset configuration Specify schema (raw_business_data or raw_telematics_data) in each dataset

Dashboard and chart import

  1. Download the files from the repository:
git clone https://github.com/yourusername/superset-dashboard.git
  1. In Superset, go to Settings → Import/Export
  2. Import the files in the following order:
    • datasets.json - datasets
    • charts.json - charts
    • dashboards.json - dashboards
  3. After importing, update the database connections in each dataset

Project structure

├── datasets/                    # Folder with datasets
│   ├── vehicles.json           # Vehicle data dataset
│   └── tracking.json           # Tracking data dataset
├── charts/                      # Folder with charts
│   ├── status_charts.json      # Status charts
│   └── movement_charts.json    # Movement charts
├── dashboards/                  # Folder with dashboards
│   └── main_dashboard.json     # Main dashboard
├── documentation/               # Documentation
│   ├── data_model.md           # Data model description
│   └── setup_guide.md          # Setup guide
└── README.md                    # This guide

Troubleshooting

Database connection issues

  • Connection error: Check the correctness of credentials and connection parameters
  • Firewall error: Ensure your IP address is added to the allowlist
  • SSL issues: Check SSL settings in connection parameters

Performance issues

  • Slow visualization loading:
    • Optimize SQL queries
    • Reduce the number of simultaneously displayed elements
    • Use result caching
  • High memory usage:
    • Increase Docker container resources
    • Optimize database queries

Other issues

Here are some tricks that can help you fix common issues:

  1. Check Superset logs:
docker-compose logs superset
  1. Restart containers:
docker-compose restart
  1. Clear browser cache
  2. Check Superset version and update if necessary

Next steps

After successfully connecting Power BI to your Private Telematics Lakehouse instance, we recommend you to:

  • Explore the available data schemas by reviewing the Schema overview section to better understand the data structure and relationships.
  • Start with simple queries focused on specific business entities before building complex dashboards - check our Example queries for reference.

Support

For technical questions or requests for access to the demonstration database, please contact: support@squaregps.com