Which health checks should be installed on input datasets of a Foundry data pipeline?

Installing a schema check on input datasets of a Foundry data pipeline is crucial because it ensures that the data being ingested adheres to a predefined structure or format. A schema check verifies that the data types, field names, and overall structure of the dataset match what the pipeline expects. This is essential for maintaining data integrity and preventing issues that may arise from mismatched or malformed data.

When data doesn't conform to the expected schema, it can lead to failures in downstream processing, erroneous analyses, and overall instability of the data pipeline. By implementing schema checks, data engineers can catch these issues early in the data ingestion process, allowing for prompt remediation and ensuring the reliability of the data throughout its lifecycle.

In contrast, while the other health checks such as build duration, data freshness, and sync status are important for monitoring general data pipeline performance and timeliness, they do not directly address the structural integrity of the input datasets.