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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 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 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.
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.
Checkpoints in Azure Data Lake Storage Gen2 allow you to track progress and resume processing from a specific point in a data engineering job.
Answer: True
Which Azure service can be used to configure watermarking during data processing?
Answer: a) Azure Stream Analytics
Which of the following streaming sources can be used with Azure Stream Analytics for watermarking?
Answer: a) Azure Event Hubs, b) Azure IoT Hub
What are the benefits of using checkpoints in data engineering jobs on Azure?
Answer: a) Fault tolerance, b) Scalability
Checkpoints can only be used in batch processing scenarios.
Answer: False
Which statement best describes watermarking in data processing?
Answer: c) It is the process of adding a timestamp to data records to indicate their arrival time.
Checkpoints in Azure Data Factory allow you to roll back processing to a specific point in time.
Answer: False
Which Azure service allows you to configure watermark delays in data processing?
Answer: a) Azure Stream Analytics
When configuring watermarking in Azure Stream Analytics, which options are available for defining the watermark delay?
Answer: a) Event time, b) Processing time
Watermarking can be used to handle data out-of-order events in data processing.
Answer: True
36 Replies to “Configure checkpoints and watermarking during processing”
How frequently should checkpoints be created for optimal performance?
It depends on your data volume and processing needs. Typically, 5-10 minutes is a good interval.
Thank you so much for this information!
Helped me configure my pipelines correctly.
Checkpoint intervals, any suggestions?
As mentioned before, it depends on your data needs, but 5-10 minutes usually is a good range.
I think some aspects weren’t covered adequately.
Great post on configuring checkpoints and watermarking in Azure Data Engineering!
Thanks for the detailed guide!
Should I use Azure Synapse or Azure Stream Analytics for checkpointing?
Depends on your use case. ASA is good for real-time analytics while Synapse is more suited for big data queries.
Clear and concise, thank you!
Why is watermarking critical in streaming data pipelines?
It ensures data completeness and correctness, especially when dealing with out-of-order events in streaming systems.
Great insights on watermarking!
Can anyone explain the process of setting up a checkpoint in Azure Stream Analytics?
Sure! To set up a checkpoint in ASA, you need to enable the ‘Event Ordering’ option and then specify the checkpoint interval.
Also, make sure to use the Azure blob storage as the state store for checkpoints.
Anyone tested this in production yet?
Yes, we have and it works fine as long as you follow the best practices.
Checkpoint vs Watermarking: which is more critical?
Both are critical but serve different purposes. Checkpoints save processing state, watermarking handles event ordering.
Can we use custom watermarking logic in Azure Data Factory?
Make sure to handle late data and out-of-order data correctly in your custom logic.
Yes, you can implement custom watermarking logic using ADF’s custom activity feature.
What are the main benefits of using watermarking in data processing?
Watermarking helps ensure that late-arriving data is handled correctly and to maintain the accuracy of time-based calculations.
Another benefit is that it helps in optimizing the performance of the streaming job by reducing unnecessary computations.
I followed the steps but still getting errors. Any suggestions?
Check if your checkpoints are being stored to the correct blob storage path and ensure the necessary access permissions are set.
Any tips for debugging checkpoint-related issues?
Start by checking your storage accounts for correct setup and permissions.
Is there any specific template to follow for implementing checkpointing?
You can refer to Microsoft’s best practices documentation for a standardized approach.
Nice explanation! Helped me a lot!
Very helpful post, thanks!