# 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. {% hint style="info" %} This guide is part of the **IoT Query** documentation suite and specifically covers connecting Power BI to your data warehouse. If you're still deciding which BI tool to use, refer to the [Selecting BI tools](/docs/analytics/iot-query/connection-setup/selecting-bi-tools.md) overview. {% endhint %} ## 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: * [Docker Installation](https://docs.docker.com/engine/install/) * [Docker Compose Installation](https://docs.docker.com/compose/install/) 2. Download the official docker-compose file: {% code overflow="wrap" %} ```sh curl -fL https://raw.githubusercontent.com/apache/superset/master/docker-compose-non-dev.yml -o docker-compose.yml ``` {% endcode %} 3. Start Superset: ```sh docker-compose up -d ``` 4. Create an administrator: ```sh docker-compose exec superset superset fab create-admin \ --username admin \ --firstname Superset \ --lastname Admin \ --email admin@superset.com \ --password admin ``` 5. Initialize the database: ```sh docker-compose exec superset superset db upgrade ``` 6. Load examples and initialize roles: ```sh docker-compose exec superset superset init ``` ### 2. Installation with pip (for development) 1. Create a virtual environment: ```sh python -m venv venv source venv/bin/activate # for Linux/macOS # or .\venv\Scripts\activate # for Windows ``` 2. Install Superset: ```sh pip install apache-superset ``` 3. Initialize the database: ```sh superset db upgrade ``` 4. Create an administrator: ```sh export FLASK_APP=superset superset fab create-admin ``` 5. Load examples and initialize roles: ```sh superset init ``` 6. Start Superset: ```sh superset run -p 8088 --with-threads --reload --debugger ``` ## Database connection 1. Log in to Superset (default: ) 2. Navigate to Data → Databases 3. Click "+" to add a new database 4. Fill in the connection parameters: 1. Database: PostgreSQL 2. SQLAlchemy URI: `postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}` 3. Display Name: Analytics Database 4. Extra: `{"engine_params": {"connect_args": {"sslmode": "require"}}}` 5. Click **Test Connection** to verify the connection 6. Save the settings ### Connection parameter reference
Lakehouse ParameterApache Superset Setting LocationNotes
HostDB_HOST in SQLAlchemy URIThe database server address provided in your welcome email
PortDB_PORT in SQLAlchemy URIDefault is 5432 for PostgreSQL
Database nameDB_NAME in SQLAlchemy URIYour assigned database name
UsernameDB_USER in SQLAlchemy URIYour database username
PasswordDB_PASS in SQLAlchemy URIYour secure database password
SSL modeconnect_args in Extra parametersSet to require in the Extra JSON configuration
SchemaDataset configurationSpecify schema (raw_business_data or raw_telematics_data) in each dataset
## Dashboard and chart import 1. Clone the [bi-integratons](https://github.com/SquareGPS/bi-intergrations) repository: ```sh git clone https://github.com/SquareGPS/bi-intergrations.git ``` 2. In Superset, go to **Settings → Import/Export** 3. Import the files in the following order: 1. `datasets.json` - datasets 2. `charts.json` - charts 3. `dashboards.json` - dashboards 4. After importing, update the database connections in each dataset ## 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 ``` 2. Restart containers: ``` docker-compose restart ``` 3. Clear browser cache 4. Check Superset version and update if necessary ## Next steps After successfully connecting Power BI to your **IoT Query** instance, we recommend you to: * Explore the available data schemas by reviewing the [Schema overview](/docs/analytics/iot-query/schema-overview.md) 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](/docs/analytics/example-queries.md) for reference. ### **Support** For technical questions or requests for access to the demonstration database, please contact: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://navixy.com/docs/analytics/iot-query/connection-setup/selecting-bi-tools/connecting-apache-superset.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.