topic Re: Best Approaches to Avoid Duplicates When Using Copy Activity to Load Data into an Existing Table in Pipelines

Best Approaches to Avoid Duplicates When Using Copy Activity to Load Data into an Existing Table

HamidBee — Sun, 03 Nov 2024 12:47:12 GMT

Hi All,

I'm using the Copy Activity in Microsoft Fabric to load data into an existing table, but I’m concerned about the risk of duplicate entries. For example, if the copy operation is interrupted and restarted, or if I unintentionally include data from previous periods, it could lead to duplicate records in my target table.

What are some recommended settings or strategies to ensure only unique records are loaded, even when there’s a chance of re-running the data or having overlapping entries from a prior load? Any insights on best practices for managing this kind of scenario would be really helpful.

Re: Best Approaches to Avoid Duplicates When Using Copy Activity to Load Data into an Existing Table

Anonymous — Mon, 04 Nov 2024 02:05:01 GMT

Hi HamidBee ,

Here are some of my personal thoughts on your question:

1. In Microsoft Fabric, you can configure the Copy Activity to perform upserts by using the Mapping Data Flow feature. Upsert combines the insert and update operations. It checks if a record already exists in the target table based on a unique key. If it exists, it updates the record; if not, it inserts a new one.

You can look at this document below:

A guide to Fabric Dataflows for Azure Data Factory Mapping Data Flow users - Microsoft Fabric | Microsoft Learn

2. Implement an incremental load strategy where only new or changed records are loaded. This can be achieved by maintaining a watermark or timestamp column to track the last loaded record.

I think you can take a look about this document:

Pattern to incrementally amass data with Dataflow Gen2 - Microsoft Fabric | Microsoft Learn

Best Regards

Yilong Zhou

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Re: Best Approaches to Avoid Duplicates When Using Copy Activity to Load Data into an Existing Table

HamidBee — Mon, 04 Nov 2024 15:31:26 GMT

Thanks for sharing.