spark-python-data-source

To create a custom Spark data source in Python, implement the DataSource class along with Reader/Writer and Stream variants following the PySpark DataSource API. This approach enables reading from and writing to external systems like databases and APIs.

Yes, the PySpark DataSource API supports both batch and streaming scenarios. You can implement custom readers and writers to ingest streaming event data or perform batch reads and writes to external message queues and REST APIs.

The best way to connect a REST API to Spark is to build a custom Python data source plugin. By implementing the DataSource and Stream classes, you can ingest streaming event data from the API directly into Spark for analytics.

Yes, for systems lacking native Spark support, you need a custom data source plugin. This Skill guides you through building a flat, dependency-light architecture using the PySpark DataSource API to connect and interact with these external systems.

A flat, dependency-light architecture for Spark data sources relies on implementing DataSource, Reader/Writer, and Stream variants directly in Python. This minimizes external dependencies while enabling batch and streaming data exchange with external systems.