Where should you install Schema Checks to monitor a data pipeline for unexpected changes in the data structure?

Installing Schema Checks on both input and output datasets is the most effective approach for monitoring a data pipeline for unexpected changes in the data structure. This ensures that you have comprehensive visibility into the data as it flows through the pipeline.

By implementing checks on the input datasets, you can validate the structure of the data before it enters the processing stages. This proactive step helps identify any issues with the incoming data that could lead to errors or inconsistencies later in the pipeline. Catching these changes early allows for timely rectification, ensuring that the integrity of the pipeline is maintained from the moment data is ingested.

Additionally, placing schema checks on the output datasets allows you to verify that the final processed data retains the expected schema after all transformations have been applied. This is crucial since any changes during processing could lead to structural inconsistencies that may not align with downstream expectations. By validating the output, you ensure that the data delivered meets the required standards, thus preventing potential issues in data consumption.

Overall, applying Schema Checks at both stages provides a more robust monitoring strategy, facilitating faster detection of issues and ensuring the reliability of the data pipeline as a whole.