Google Cloud Professional Data Engineer Exam

When dealing with inconsistent input data in a Dataflow pipeline, what is the recommended approach to capture erroneous data efficiently?

1. Re-read the input data and create separate outputs for valid and erroneous data.

2. Read the data once, and split it into two pipelines, one for valid and another for erroneous data.

3. Check for the erroneous data in the logs.

4. Create a side output for the erroneous data.

The correct answer is: Create a side output for the erroneous data.

The recommended approach to capture erroneous data efficiently in a Dataflow pipeline is to create a side output for the erroneous data. This method leverages the capabilities of Apache Beam, which underlies Dataflow, to handle cases where the input data may contain errors. By using side outputs, you can design your pipeline to process the main flow of valid data while simultaneously routing any erroneous records to a separate output, known as a "side output." This allows you to maintain the integrity and performance of your primary data processing without interrupting the flow for error-handling operations. The side output provides a dedicated flow for troubleshooting and inspection, enabling developers or data engineers to analyze the erroneous data later without losing context or affecting the main data processing logic. This approach is particularly efficient because it optimally utilizes the pipeline's resources, allowing for real-time processing of valid data while also collecting and managing errors in a structured manner. In contrast, other options may involve re-scanning the dataset or relying on logs, both of which could introduce unnecessary complexity or inefficiency to the data processing workflow.