Query on Pulling Multi-Dimension Reports at Day Granularity Using Adobe Analytics 2.0 in Power BI

[gbhoosamRavi]

Hello Power BI Community,

I'm currently facing a challenge with the Adobe Analytics 2.0 connector in Power BI. My goal is to pull multi-dimensional reports at a day granularity for a year-to-date (YTD) period. However, I'm encountering two primary issues:

1. Feasibility: Is it possible to pull multi-dimensional reports at a day granularity for a YTD period using the Adobe Analytics 2.0 connector in Power BI? I am able to pull the data with single dimension

2. Performance: Each time I attempt to pull the data, it takes a significant amount of time and eventually times out. Is there a way to optimize this process or is this expected due to the volume and granularity of the data?

Any insights, tips, or guidance on how to efficiently achieve this task would be greatly appreciated.

Thank you in advance for your help!

[ReportMaster]

Hi @gbhoosamRavi were you able to find a solution? As a workaround, maybe you can try testing your connection with a 3rd party connector. I currently use windsor.ai's Adobe connector and I don't have any issue when building my reports using any of the available fields or metrics. In case you wonder,  to make the connection first search for the Adobe connector in the data sources list:

Then, you should enter your Adobe credentials to make the connection. After that, you'll see a preview of your data where you can select the fields you want to see in PBI. 

Finally, you just have to choose PBI as destination and copy and paste the url that will appear there in PBI desktop --> Get Data --Web > --> Paste the link

[Sahir_Maharaj]

Hello @gbhoosamRavi,

1. Adobe Analytics 2.0 Connector should theoretically support pulling multi-dimensional reports. However, the complexity increases when adding more dimensions and granularity, especially at a day level over a long period like YTD (Adobe might have limitations on the amount of data that can be fetched in a single query)

2. High data volume can lead to long processing times (and timeouts) as this is expected due to the amount of data being processed. 

• If possible, consider pulling the data at a higher level of granularity (e.g., weekly or monthly) and then breaking it down into daily granularity.
• It may also be worthwile implementing incremental data loading.
• Also, instead of pulling all dimensions in a single query, maybe consider splitting them up and combining the data in Power BI.

Hope this helps - If you require any questions or further assistance, please do not hesitate to reach out to me.