CDC implementation for Dataverse table in fabric using delta links

[Ymatole]

I am looking for efficient incremental load method for dataverse tables. Not sure if dataverse tables have watermark column or any other efficient way available to implement CDC.

[deborshi_nag]

Hi @Ymatole 

You can achieve CDC with Dataverse. The key concept is zero‑copy integration via OneLake shortcuts created by the Link to Microsoft Fabric feature—this exposes Dataverse tables in a Fabric lakehouse as Delta/Parquet without exporting or ETL, and keeps them continuously in sync.

Your options are:

Link to Microsoft Fabric

• From Power Apps, you link a Dataverse environment to a Fabric workspace. Fabric automatically creates a Lakehouse, SQL endpoint, Power BI dataset, and OneLake shortcuts for your Dataverse tables. Changes in Dataverse flow through and appear in Fabric with no copies or pipelines.

Create Dataverse shortcuts directly from Fabric

• If you want to pick specific tables (instead of bringing the whole environment), you can create shortcuts to Dataverse inside a Lakehouse directly in Fabric. This still uses the Dataverse managed lake behind the scenes and is read‑only

Existing Azure Synapse Link → Fabric

• If you already use Synapse Link for Dataverse to land Delta tables in ADLS Gen2, you can create a OneLake shortcut to that ADLS from your Fabric Lakehouse, reusing your existing link profile—so you can adopt Fabric without changing your current extraction path.

Hope this gives you an overview on options available on Fabric for you. If this helps, please Accept as a solution. 

[v-sgandrathi]

Hi @Ymatole,

Thank you @nielsvdc for your response.

Along with the previous points, remember that when using incremental loads based on modifiedon, it’s important to store the last successfully processed timestamp externally, such as in a control table or pipeline parameter, to prevent data gaps or duplicates during retries.

If you need high-volume or near–real-time sync, be sure to check Dataverse API limits and throttling, as these can affect large incremental loads. Fabric Link can assist, but monitoring sync latency and full-load performance is still necessary.

Finally, make sure that Change Tracking is enabled at the table level and is supported for your specific Dataverse entities, since not all system or virtual tables support CDC in the same way.

Thank you.

[v-sgandrathi]

Hi @Ymatole,

we haven't heard back from you regarding our last response and wanted to check if your issue has been resolved.

Should you have any further questions, feel free to reach out.
Thank you for being a part of the Microsoft Fabric Community Forum!

[v-sgandrathi]

Hi @Ymatole,

I wanted to follow up on our previous suggestions regarding the issue. We would love to hear back from you to ensure we can assist you further.

Thank you.

[nielsvdc]

Hi @Ymatole, dataverse tables do include a watermark column that is the modifiedon field, which records the last update timestamp for each row. This makes incremental loading straightforward by filtering rows where modifiedon is greater than the last processed timestamp. For a more robust CDC approach (including deletes), you can enable Change Tracking in Dataverse.

But, an even more efficient option is to use Fabric Link for Dataverse, which streams changes into Fabric Lakehouse in near real time, eliminating the need for manual incremental logic. You can learn more about this approach here: https://learn.microsoft.com/en-us/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric.

Hope this helps. If so, please give kudos 👍 and mark as Accepted Solution ✔️ to help others.