Limitations of Data Sources Handled by Looker Studio

[SoukainaELMIRI]

Hi,

I am new to Looker Studio and working on creating dashboards. I have the following data sources, and I would like to know if Looker Studio can handle all of them. I really need your help to avoid wasting time.

Data sources:

• BigQuery
• Google Ads
• DV360
• Meta
• Pinterest
• Criteo
• Google Analytics
• Amazon

Thank you in advance for your help!

[sam8]

These are all supported in some capacity: 

• BigQuery: first party connector available
• Google Ads: first party connector available
• DV360: community connector available
• Meta: community connector available
• Pinterest: community connector available
• Criteo: community connector available
• Google Analytics: first party connector available
• Amazon: community connector available

[SoukainaELMIRI]

Thank you @sam8  for your response!

To clarify, my question is not just about the availability of connectors, but also about data blending capabilities in Looker Studio. I want to create a complete dashboard that combines multiple data sources.

For example, I need to calculate the total budget spent, which means summing up the budget from Google Ads, DV360, Meta, Pinterest, and Criteo. Can Looker Studio handle this type of data blending across different sources? If so, what is the best approach to ensure accurate aggregation and avoid data mismatches?

Thank you again for your help.

[sam8]

Ah, i see what you mean. 

In theory, that should be fine! You can get the budget from each of those data sources, blend the sources together, and then create a new field which adds together each of those budget fields.

In practice, you are right to worry about data mismatches. Unless you can join on very concrete dimensions like IDs, you'll likely end up with some duplication. I'm not familiar enough with those five data sources to know how well the data would blend. If these sources don't have much in common and you end up joining on a dimension like a date, you'd be stuck manually identifying the duplication factors and then dividing by those in your new metrics. I talked about this in another reply. Not very robust, but can get the job done.

[阿泰]

Q: If so, what is the best approach to ensure accurate aggregation and avoid data mismatches?

A: I think the biggest issue with data blending is that each data source may not have the same columns (dimensions) to blend, such as "date," "user_id," or "product_id."

In summary, I believe it’s important to ensure that all data sources have the same columns. If not, consider how to create or derive a new column to align the data.

For example, when blending Meta Ads with GA4 data, I check whether both sources have a common column. To facilitate data blending, I prepare a UTM_Campaign column to ensure consistency between Meta and GA4.