Exploring Optimal Data Storage Solutions in Power BI

[Anonymous]

We are seeking a suitable solution for storing data in Power BI using in-memory mode. To optimize data storage, we need to determine the most efficient approach.

Some potential solutions being considered include:

1. Azure Databricks Delta Table Refresh: Is refreshing data from Azure Databricks delta tables to Power BI, using incremental refresh, a viable approach?
2. Azure Databricks Delta Table with JSON Data and Explode: Would storing JSON data in Azure Databricks delta tables and using the "explode" function /simlie in Power BI be a smart and efficient method?

The goal is to find the best approach to store data in Power BI's in-memory mode, ensuring optimal performance and data management.

[Anonymous]

Hi @Anonymous ,

Have you solved your problem? If it is solved, please share your solution and accept it as solution or mark the helpful reply, it will be helpful for other members of the community who have similar problems as yours to solve it faster. Thank you very much for your kind cooperation!

Best Regards,
Zhu

[Anonymous]

problem not yet sloved 🙂 still exist. increasing the p4 to p5 doubling the cost 

[Anonymous]

Hi @Anonymous ,

It is recommended to use incremental refresh, combined with a time field to filter. If you need to synchronize data in real time, you need to combine with stream processing or DLT. If using a delta table, it can also be achieved in conjunction with a lakehouse or warehouse and dataflow.

Related links:

databricks streaming with power bi

Load and process data incrementally with Delta Live Tables flows - Azure Databricks | Azure Docs

Incremental refresh for materialized views - Azure Databricks | Microsoft Learn

Best Regards,
Zhu

If there is any post helps, then please consider Accept it as the solution  to help the other members find it more quickly.