How power BI handle large DataSet?

Sorry Maggi029, I am not sure how Python ties into your PowerBi integration.

I query Elasticsearch directly with PowerBi and use pagination to get all results on GBs of data. 

However, I recommend looking into Elasticsearch Data Transforms that allow you to aggregate large sets of data into a new index with the value you need instead of working with raw documents.

https://www.elastic.co/guide/en/elasticsearch/reference/current/transform-overview.html

An example is have 10,000 documents with the data such as:
{"computer_name": "computer1", "organzation" : "Finance", "country" : "United States of America"}

{"computer_name": "computer2", "organzation" : "Finance", "country" : "United States of America"}

....
{"computer_name": "computer10000", "organzation" : "Finance", "country" : "United States of America"}

You can use transforms to get counts, averages, etc.. to reduce the data down to just 3 documents.

{"unique_computer_names": "700", "organzation" : "Finance", "country" : "United States of America"}
{"unique_computer_names": "100", "organzation" : "Human Resources", "country" : "Nigeria"}
{"unique_computer_names": "200", "organzation" : "Information Technology", "country" : "France"}

Working at something like this at scale can take billions of documents to thousands of documents when you narrow in on specific data you wish to display high level metrics in PowerBi. 

This might be a lot of work and research but you can dramatically reduce large datasets by creating summaries of the data and display what is needed. Not a quick solution by any means but it helps PowerBi out by using summarized data.