spark-python-data-source

Build a custom Spark data source connector in Python by implementing the PySpark DataSource API to create batch and streaming readers and writers for external systems like databases, REST APIs, and message queues.

Yes, PySpark can read and write to external systems without native Spark support by developing custom data source connectors that implement batch and streaming ingestion and egress for proprietary APIs and file formats.

The best way to connect Spark to a proprietary REST API is to build a custom data source connector using Python, allowing you to read and write data as if it were a standard Spark table.

Yes, the PySpark DataSource API supports both batch and streaming data integration, enabling you to implement custom readers and writers for real-time message queues and standard batch database ingestion.

Test a custom PySpark data connector using pytest and pytest-spark to validate your batch and streaming reader and writer implementations against the PySpark DataSource API.

Implementing a custom Spark data source requires PySpark knowledge, but the approach focuses on simplicity, explicit control flow, and standard library usage to streamline custom connector development.