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SteveShen
New Member

How to optimize PowerBI Report with Oracle data source that refreshes over 5 hours

Hi Community members, 

Here is the situation: Our PowerBI report use Oracle data source to import several tables, the data size is roughly millions.

When release the report, it always shows overtime and fails. 

 

If maintaining current data size, is there any optimizing solution? (Server / PowerBi Operation / Data Source)

 

Or does PowerBI itself has some limits in dealing with large data voulme?

1 ACCEPTED SOLUTION
v-ssriganesh
Community Support
Community Support

Hi @SteveShen 
Thanks for posting your query in fabric community forum.

To optimize your Power BI reports and to better handle large data volumes, here's what you can do:

1. Make sure that your Oracle queries are optimized to return only necessary data. This reduces both the load on the Oracle server as well as Power BI.
2. Preprocess and reduce the data before it reaches the Power BI service by using Power BI dataflows. This will help to manage large datasets more efficiently.
3. Make sure your Power BI service is on an appropriate capacity (e.g., Premium) that can handle large datasets efficiently. Higher capacity provides more resources for processing. Power BI has a limit of 1 GB for datasets in the Pro version and up to 400 GB in Premium. However, the performance can degrade as data size increases, even within these limits.you can manage your capacity by referring this document link

Manage your Fabric capacity - Microsoft Fabric | Microsoft Learn

4. When using an on-premises data gateway, ensure the gateway is properly configured with adequate resources. Also, a dedicated gateway can enhance performance.
5. In case queries take too much time to execute, Power BI might timeout. The timeout defaults to 10 minutes, which can be changed in the settings.
6. You can optimize your solution at different architectural layers, including the data source, data model, and visualizations. For more information, you can refer to the link
Optimization guide for Power BI

There are several techniques, such as optimizing rows and filtering source data, for handling large volumes of data and improving responsiveness in Power BI. You can read more on these techniques in the article How to Handle Large Volumes of Data in Power BI

If this post helps you, please accept it as the solution so other members can find it more quickly.
Hope this helps!
Thanks.

View solution in original post

2 REPLIES 2
v-ssriganesh
Community Support
Community Support

Hi @SteveShen 
Thanks for posting your query in fabric community forum.

To optimize your Power BI reports and to better handle large data volumes, here's what you can do:

1. Make sure that your Oracle queries are optimized to return only necessary data. This reduces both the load on the Oracle server as well as Power BI.
2. Preprocess and reduce the data before it reaches the Power BI service by using Power BI dataflows. This will help to manage large datasets more efficiently.
3. Make sure your Power BI service is on an appropriate capacity (e.g., Premium) that can handle large datasets efficiently. Higher capacity provides more resources for processing. Power BI has a limit of 1 GB for datasets in the Pro version and up to 400 GB in Premium. However, the performance can degrade as data size increases, even within these limits.you can manage your capacity by referring this document link

Manage your Fabric capacity - Microsoft Fabric | Microsoft Learn

4. When using an on-premises data gateway, ensure the gateway is properly configured with adequate resources. Also, a dedicated gateway can enhance performance.
5. In case queries take too much time to execute, Power BI might timeout. The timeout defaults to 10 minutes, which can be changed in the settings.
6. You can optimize your solution at different architectural layers, including the data source, data model, and visualizations. For more information, you can refer to the link
Optimization guide for Power BI

There are several techniques, such as optimizing rows and filtering source data, for handling large volumes of data and improving responsiveness in Power BI. You can read more on these techniques in the article How to Handle Large Volumes of Data in Power BI

If this post helps you, please accept it as the solution so other members can find it more quickly.
Hope this helps!
Thanks.

Great thanks! We'll take your advice in consideration.

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