How to Use Flink SQL, Streamlit, and Kafka: Part 1

A tutorial outlines the first phase of building a live market-data application with Streamlit, Apache Kafka, and Apache Flink using SPY bid prices from the Alpaca Market Data websocket. A Python Kafka producer serializes incoming quote records with a JSON schema and sends them to a Confluent Cloud topic, enabling low-latency ingestion without REST request cycles. Flink SQL then processes the Kafka-backed data through five-second tumbling windows, calculating average bid prices and writing the results to an output topic containing window start and end times alongside price values. These processed records are intended to drive a real-time Streamlit chart, although the article notes that running Kafka producer and consumer tasks concurrently in the same Streamlit application introduces multithreading and asynchronous programming challenges. A subsequent installment is planned to address those issues, Alpaca rate limits, and the final visualization implementation.

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