data-pipeline

You can build a data pipeline by defining a multi-step transformation DAG and applying schema validation at each stage to ensure data quality. Configurable error handling strategies automatically manage failures during the ETL process.

A DAG-based data pipeline models data transformations as a directed acyclic graph of sequential steps. You need this approach for batch ETL tasks and streaming data prep when processing large datasets that require structured, auditable workflows.

You configure parallel processing by setting configurable worker counts within your data pipeline. This allows the workflow to process large datasets concurrently, improving throughput for batch ETL tasks and data validation workflows.

Yes, you can implement auditable logging to track transformations for compliance alongside configurable error handling strategies. The pipeline supports skip, retry, or fail behaviors to manage data validation exceptions reliably.

The best way to handle data validation failures is to use built-in configurable error strategies within your pipeline. You can set the workflow to skip invalid records, retry processing, or immediately fail based on your data quality requirements.

Yes, you need to run the setup script to install required dependencies before building your first pipeline. This prepares your Python environment for defining multi-step transformations, schema validation, and parallel processing.