Configure checkpoints and watermarking during processing

When working with large datasets in a data engineering project on Microsoft Azure, it is important to ensure data integrity and track progress during processing. This can be achieved through the use of configuration checkpoints and watermarking. In this article, we will explore how to configure these features in your data engineering pipeline on Azure.

Checkpoints

Checkpoints are markers that indicate the progress of data processing. They allow you to resume processing from the point of failure in case of any issues or interruptions. Azure Data Factory provides checkpointing functionality that can be utilized in your data engineering pipeline.

To configure checkpoints, you need to define a storage account in Azure Blob storage where the checkpoint data will be stored. This storage account should be accessible by your Azure Data Factory. Once you have the storage account ready, you can configure it as a checkpoint location for each relevant activity in your pipeline.

For example, let’s consider a scenario where you are ingesting data from an external source into Azure Data Lake Storage. You can configure the copy activity in your pipeline to use checkpoints by specifying the checkpoint location as follows:

{
“type”: “AzureBlobFSSnapshot”,
“linkedServiceName”: {
“referenceName”: “CheckpointStorageLinkedService”,
“type”: “LinkedServiceReference”
},
“folderPath”: “checkpoint-folder-path”
}

In the above code snippet, “CheckpointStorageLinkedService” refers to the linked service representing the storage account, and “checkpoint-folder-path” specifies the folder path where the checkpoint data will be stored.

By enabling checkpoints, Azure Data Factory will track the progress of the copy activity and store the checkpoint data in the specified storage account. In case of any failures, the pipeline can be restarted from the last successful checkpoint, saving time and resources.

Watermarking

Watermarking allows you to mark the progress of data processing in a specific column. This is useful when you have incremental data updates and need to process only the newly added or modified records.

To configure watermarking, you need to define a watermark column in your dataset. This column should contain a timestamp or an incrementing value. Azure Data Factory uses the watermark column to identify the latest processed record during subsequent runs.

Let’s consider an example where you have a dataset with a timestamp column named “lastModified”. You can configure watermarking for this column as follows:

“watermark”: {
“value”: “@trigger().outputs.body.timestamp”,
“condition”: “lastModified > ‘@trigger().outputs.body.timestamp'”
}

In the above code snippet, “@trigger().outputs.body.timestamp” represents the timestamp value provided by the trigger that initiates the pipeline run. The watermark condition ensures that only records with a greater timestamp value than the last processed record are selected for processing.

By utilizing watermarking, your data engineering pipeline can efficiently process only the incremental data, reducing the processing time and cost.

Conclusion

Configuring checkpoints and watermarking in your data engineering pipeline on Microsoft Azure is crucial for ensuring data integrity and tracking progress. Azure Data Factory provides the necessary functionality to enable these features, allowing you to resume processing from the point of failure and efficiently process incremental data. By implementing these techniques, you can enhance the reliability and efficiency of your data engineering workflows on Azure.