Developer Documents Multi-Layer Debugging Journey to Connect PySpark with Kafka

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A developer working on a financial data engineering project attempted to extend a batch pipeline with Spark Structured Streaming by reading JSON messages from a Kafka topic. Although the Python Kafka producer and Kafka broker were functioning correctly in Docker, running the Spark job triggered a chain of failures across multiple layers including PySpark, the Spark runtime, Kafka connectors, Hadoop on Windows, Docker networking, and dependency resolution. The spark-submit launcher failed to locate the pip-installed Spark environment on Windows, prompting the developer to run the Python script directly instead. This workaround bypassed the launcher issue but immediately revealed a second problem: Spark lacked the Kafka data source connector entirely, meaning it could not even attempt a connection. The experience highlighted the importance of isolating failures by layer rather than assuming all errors originate in application-level Python code.

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