Debugging and Troubleshooting Data Pipelines
Explore various debugging and troubleshooting options available for ADF pipelines.
Debugging and troubleshooting are important aspects of the data pipeline development life cycle. While designing and implementing data pipelines, developers often encounter errors that need to be identified and resolved quickly to ensure smooth data integration. In this lesson, we’ll explore various debugging and troubleshooting techniques available in Azure Data Factory.
Debugging and troubleshooting concepts
Pipeline validation: Before running a pipeline, it is important to ensure that the pipeline is valid and that all connections and dependencies are properly configured. Azure Data Factory provides a validation feature that enables developers to validate a pipeline for any syntax or semantic errors.
Pipeline monitoring: Azure Data Factory provides detailed monitoring options to track the status of a pipeline. Pipeline runs can be monitored in real time, and logs can be collected to identify the root cause of any errors.
Debugging in Visual Studio Code: Visual Studio Code is a popular integrated development environment (IDE) for Azure Data Factory. It provides a powerful debugging feature that enables developers to set breakpoints, inspect variables, and step through code to identify and resolve issues.
Debugging using Azure CLI: Azure CLI provides several commands that can be used for debugging pipelines. Developers can use the
adf pipeline create-run
command to create a pipeline run, and theadf pipeline show-run
command to view the status of the pipeline run. Theadf pipeline cancel-run
command can be used to cancel a pipeline run if necessary.Troubleshooting common errors: Some common errors that developers may encounter while working with pipelines include connection timeouts, authentication errors, and data type mismatches. Azure Data Factory provides detailed error messages that can be used to identify the root cause of the issue.
Debugging using Azure CLI
Create and cancel pipeline runs
List out all available data pipelines using the Azure CLI command below:
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