Configure error handling for a transformation

Transforming data is an essential task in any data engineering workflow. When working with data transformations in Microsoft Azure, it is crucial to configure error handling effectively. Error handling ensures that data processing pipelines continue to run smoothly even when errors occur. In this article, we will explore how to configure error handling for a transformation in Azure, specifically using Azure Data Factory.

Azure Data Factory Overview

Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and orchestrate data pipelines. By leveraging Azure Data Factory’s capabilities, you can easily handle errors in your data transformation processes.

Steps to Configure Error Handling

To configure error handling for a transformation in Azure, you can follow these steps:

1. Create an Azure Data Factory pipeline: Start by creating an Azure Data Factory pipeline that includes the transformation activity you want to configure error handling for. You can create a new pipeline or use an existing one.
2. Configure the transformation activity: Within the pipeline, configure the transformation activity that performs the data transformation. This can include data transformations such as mapping data fields, aggregating data, or filtering data. Ensure that you have defined the input datasets, output datasets, and any necessary transformations in the activity settings.
3. Enable error handling: To enable error handling for the transformation activity, navigate to the settings of the activity. Under the “Settings” section, you will find an option called “Error handling”. Enable this option to configure error handling for the transformation.
4. Configure error handling properties: Once error handling is enabled, you can configure various error handling properties. These properties allow you to define the behavior of the transformation activity when errors occur.
• Maximum number of retries: Specify the maximum number of times the activity should retry in case of an error. You can set the number of retries to 0 for no retries, or define a specific number of retries.
• Retry interval: Set the interval between each retry attempt. This interval allows you to control the delay between retry attempts, giving the system enough time to recover from any transient errors.
• Error threshold: Define the error threshold that determines the maximum number of errors allowed within a specific timeframe. If the number of errors exceeds this threshold, the activity fails.
• Error output: Specify where the error records should be stored when errors occur. You can choose to store the error records in a separate file, table, or sink to analyze and process them separately.
• Linked service for error output: Configure the linked service that defines the destination where the error records will be stored. This linked service must be defined in Azure Data Factory and connected to the target storage or database.
• Error handling policy: You can define a specific policy for handling errors. This policy determines the behavior when the activity encounters an error, such as skipping or failing the activity.
5. Test and monitor the pipeline: After configuring error handling, thoroughly test the pipeline to ensure it behaves as expected. Monitor the pipeline execution and verify that error records are captured correctly, and the pipeline recovers from errors according to the configured behavior.

By configuring error handling for your transformations in Azure Data Factory, you can ensure the resilience and reliability of your data engineering workflows. Handle errors effectively and take necessary actions to process problematic data records while maintaining the overall integrity of your data.

Here’s an example of how the error handling configuration may look in JSON format within an Azure Data Factory pipeline:

{
"name": "SampleTransformationActivity",
"type": "Mapping",
"linkedServiceName": {
"referenceName": "AzureBlobStorageLinkedService",
"type": "LinkedServiceReference"
},
"typeProperties": {
"source": {
"type": "AzureBlobStorageSource",
"recursive": true
},
"sink": {
"type": "AzureSqlSink",
"writeBatchSize": 10000
},
"mapper": {
"type": "TabularTranslator",
"mappings": {}
},
"enableErrorHandling": true,
"errorHandling": {
"maximumRetry": 3,
"retryIntervalInSeconds": 60,
"errorThreshold": 10,
"linkedServiceName": {
"referenceName": "AzureBlobStorageErrorSinkLinkedService",
"type": "LinkedServiceReference"
},
"linkedServiceForErrorOutput": {
"referenceName": "AzureSqlDatabaseLinkedService",
"type": "LinkedServiceReference"
},
"errorHandlingPolicy": "SilentlyContinue"
}
},
...
}

In summary, configuring error handling for transformations in Azure using Azure Data Factory is crucial for maintaining reliable data pipelines. By enabling error handling, defining properties such as retries, error thresholds, and error outputs, you can handle errors seamlessly and ensure that your data transformation processes are robust and resilient.