To set up real-time data streaming in Apache Superset, follow these steps:
1. Install and configure a message broker: Choose a suitable message broker like Apache Kafka or Redis for real-time data ingestion. Install and configure it according to the documentation.
2. Set up a data producer: Create a data producer that sends events to the message broker. This can be an application, service, or script generating real-time data.
3. Configure a stream processing engine: Use a stream processing engine like Apache Flink or Apache Beam to process the incoming data from the message broker. Set up necessary transformations, aggregations, and windowing functions as required by your analytics use case.
4. Connect Superset to the stream processing engine: In Superset’s database configuration, add a new connection to the stream processing engine using SQLAlchemy URI format. Ensure you have the appropriate Python libraries installed for the chosen engine.
5. Create a real-time table: Define a table schema in Superset that matches the output of your stream processing engine. Mark this table as “real-time” in the table settings.
6. Build charts and dashboards: Using Superset’s visualization tools, create charts and dashboards based on the real-time table. These visualizations will update automatically as new data arrives.
