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I'm a little confused as to the many options I've seen online on how to use Amazon Redshift as a data source.
The two option I am considering at the moment:
1. Use a Dataflow to connect to Redshift. This was the solution initially proposed by the client. However, as I understand, Dataflows are used for data transformation. The tables we have on Redshift are intended to be "final" tables, they're essentially ready to be visualized straight away.
2. Just connect straight to Redshift on Desktop, publish the report, and then schedule refreshes from there.
Our data is fairly small (less than 5 MB) and is not expected to grow particularly large. Can someone help make the two alternatives clearer? What are the pros and cons?
Solved! Go to Solution.
Hi @DaxPadawan
If you have all the data ready I would then use option 2, I always say keep it as simple as possible.
Hi , @DaxPadawan
Based on your inquiry questions, here are some answers to your questions based on my own understanding, you currently have two options that you need to recommend which is convenient for us to choose.
My understanding is that this choice is based on the complexity of the data transformation process (ETL) you need to do to connect to the Redshift data source, the more complex the ETL process, the more conversions the data refresh thing has to handle, the more time and resources the data refresh needs to consume after you connect directly, if the time limit is exceeded (2 hours for shared capacity, 5 hours for dedicated capacity), then it is highly recommended that you use data flow as a middle layer to execute the ETL data transformation process first. After the dataflow refresh succeeds, a refresh of the pointed dataset is performed. If the data transformation process is not complicated, it is recommended that you connect directly to the generated dataset.
For more information, you can refer to :
Connect to data created by Power Platform dataflows in Power BI Desktop - Power BI | Microsoft Learn
Thank you for your time and sharing, and thank you for your support and understanding of PowerBI!
Best Regards,
Aniya Zhang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly
Hi , @DaxPadawan
Based on your inquiry questions, here are some answers to your questions based on my own understanding, you currently have two options that you need to recommend which is convenient for us to choose.
My understanding is that this choice is based on the complexity of the data transformation process (ETL) you need to do to connect to the Redshift data source, the more complex the ETL process, the more conversions the data refresh thing has to handle, the more time and resources the data refresh needs to consume after you connect directly, if the time limit is exceeded (2 hours for shared capacity, 5 hours for dedicated capacity), then it is highly recommended that you use data flow as a middle layer to execute the ETL data transformation process first. After the dataflow refresh succeeds, a refresh of the pointed dataset is performed. If the data transformation process is not complicated, it is recommended that you connect directly to the generated dataset.
For more information, you can refer to :
Connect to data created by Power Platform dataflows in Power BI Desktop - Power BI | Microsoft Learn
Thank you for your time and sharing, and thank you for your support and understanding of PowerBI!
Best Regards,
Aniya Zhang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly
Hi @DaxPadawan
If you have all the data ready I would then use option 2, I always say keep it as simple as possible.
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