spark-python-data-source

To build a custom Spark data source connector in Python, implement batch and streaming readers and writers using a flat, single-level inheritance structure. This approach ensures maintainability and simplicity when integrating external systems like APIs or message queues.

Yes, you can use PySpark to read streaming or batch data from a paginated REST API. The framework guides the implementation of custom readers and writers to handle external systems lacking native Spark support, including proprietary APIs.

Yes, Apache Spark 4.0+ supports custom Python data source connectors for both batch and streaming data processing. This allows you to develop tailored readers and writers for external systems like message queues and databases.

The best way to structure a PySpark connector is using a flat, single-level inheritance structure. Adhering to this design principle ensures the custom data source integration remains maintainable and simple over time.

You should create a custom Spark connector when you need to integrate with external systems like proprietary databases, message queues, or APIs that lack native Spark support. This framework provides the necessary readers and writers for these custom integrations.

Custom PySpark data sources may fail streaming workloads if they do not correctly implement the required streaming reader and writer interfaces. Following the framework's specific design principles for batch and streaming ensures proper data flow.