spark-python-data-source

To build a custom PySpark data source for an external API, implement the PySpark DataSource API using a flat inheritance template for readers and writers. This provides option handling, partitioning, and type conversion.

You need a custom Spark data source when connecting to external systems lacking native Spark connectors, such as specific REST APIs, message queues, or databases requiring custom protocols for batch and streaming read/write tasks.

Yes, this approach supports both batch and streaming workloads in Spark. The unified architecture provides templates for streaming components alongside batch readers and writers to handle continuous data ingestion.

To handle authentication and partitioning in a Python Spark connector, apply built-in patterns for multi-method authentication and various strategies like time-based, token-range, or ID-range partitioning.

The best way to add error handling and retries to a PySpark data source is to implement the provided resilience patterns, which include circuit breakers, retry logic, and dead-letter queues for failed operations.

Yes, you can use this PySpark DataSource template with Databricks. It is designed to satisfy the PySpark DataSource API requirements and includes guidelines for logging, observability, and production security validation.