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I will not go into detail about aggregations themselves, as there is an awesome article written by Shabnam Watson, where you can learn more about this powerful feature.

Synoptic Panel might be where I'd start, but wondering if even the scatter could still work for you if you just add conditional formatting to it? So, I achieved the starting request to fit the data in less than 1GB, as I managed to reduce the data model size from ~1GB GB to 17MB, while at the same time preserving the original data granularity!

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Until I found Phil Seamark’s blog post about filtered aggregations that opened a whole new perspective! The original request was to move a data model from SSAS cube to Power BI Pro workspace, without losing any of 200 million rows from the fact table! Essentially, the idea is to separate “hot” and “cold” data, and then leverage the Composite model feature in Power BI – importing the “hot” data in Power BI, while “cold” data will stay in the data warehouse and be targeted through Direct Query. It will place the copy in the same directory as the original and add a suffix as defined in the settings tab.Now, you would assume that if we want to see only the data from the previous 3 months, Power BI will go only to an aggregated table and quickly pull the data and render the visual for us. The potential for SM scenes to subvert or reveal traditional gendered dynamics was discussed and themes of distinguishing fantasy from reality and the importance of choice emerged.