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How did you implemented incremental data loading in Azure Data Factory?
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#azuredatafactory #azuredataengineering #DataEngineering #dataengineeringessentials #ETL #DataEngineerTrainings #CloudDataPipeline #IncrementalLoading #AzureTutorial #DataWarehouse #DeltaLoading
How did you implemented ADF in your projects?
In this interview question and answer video, I’ll walk you through how I implemented incremental delta loading in Azure Data Factory (ADF) for a retail client project. With over 500 GB of historical sales and inventory data, I explain how we handled the initial full load and optimized daily updates using the LastModifiedDate column as a watermark.
This approach significantly reduced data transfer volumes, bringing processing time down from hours to minutes, and saved on compute and storage costs.
Key Topics Covered:
• Initial full data load with Azure Data Factory’s Copy Data Activity.
• Using LastModifiedDate as a watermark for identifying new and updated records.
• Setting up a control table in the destination database to track the high watermark.
• Building an efficient pipeline using Lookup Activity, Copy Data Activity, and Stored Procedure Activity.
• Scheduling incremental updates to run every 24 hours.
• Real-world insights into optimizing performance and saving costs.
Who Should Watch This Video?
• Data Engineers working on ETL/ELT pipelines.
• Professionals learning Azure Data Factory.
• IT experts optimizing large-scale data workflows.
💬 need more Azure data engineer Questions? Drop them in the comments below!
📢 Don’t forget to like, share, and subscribe for more Azure Data Engineer tips and tricks.
How did you implemented ADF in your projects?
In this interview question and answer video, I’ll walk you through how I implemented incremental delta loading in Azure Data Factory (ADF) for a retail client project. With over 500 GB of historical sales and inventory data, I explain how we handled the initial full load and optimized daily updates using the LastModifiedDate column as a watermark.
This approach significantly reduced data transfer volumes, bringing processing time down from hours to minutes, and saved on compute and storage costs.
Key Topics Covered:
• Initial full data load with Azure Data Factory’s Copy Data Activity.
• Using LastModifiedDate as a watermark for identifying new and updated records.
• Setting up a control table in the destination database to track the high watermark.
• Building an efficient pipeline using Lookup Activity, Copy Data Activity, and Stored Procedure Activity.
• Scheduling incremental updates to run every 24 hours.
• Real-world insights into optimizing performance and saving costs.
Who Should Watch This Video?
• Data Engineers working on ETL/ELT pipelines.
• Professionals learning Azure Data Factory.
• IT experts optimizing large-scale data workflows.
💬 need more Azure data engineer Questions? Drop them in the comments below!
📢 Don’t forget to like, share, and subscribe for more Azure Data Engineer tips and tricks.
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