spark-python-data-source

To build a custom PySpark data source in Python, you implement a flat inheritance structure for DataSource, DataSourceReader, and DataSourceWriter using ready-to-adapt templates that bridge Spark with external systems.

Yes, you can read streaming data into Spark from REST APIs using Python-based PySpark DataSource connectors, which support both batch and streaming readers without relying on Java or Scala code.

PySpark DataSource API supports writing to message queues in streaming workflows by providing transactionally-safe DataSourceWriter and streaming variant templates that export transformed results directly to external destinations.

The best way to ensure production-grade error handling in a PySpark data source is to enforce robust error handling, in-executor imports for compatibility, and minimal dependencies within your custom Python connector implementation.

No, you do not need Java or Scala dependencies to ingest SaaS API data into Spark, as the PySpark DataSource API allows developers to build custom Python connectors using a flat inheritance pattern with minimal external dependencies.

You should use a flat inheritance structure for PySpark batch and streaming readers when building transactionally-safe custom connectors that require in-executor imports and minimal dependencies for bridging Spark with external systems.