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Alisea_MI
Resolver II
Resolver II

What Takes Up Storage?

Hi, Community!

 

Our organization has just started to use Lakehouses in Fabric: both centrally by Group It and on the business side.


In the MS Fabric Capacity Metrics report, I notice, that there's 1 business user workspace, that takes up over 60% of our capacity storage - 3 times as much as our own IT lakehouse storage. Please, see the picture.

The Fabric Capacity Metrix report does not specify, which items in the workspace take most capacity - or does it?
So, my question is: what does potentially take so much capacity: Lakehouses, notebooks, somethings else? How can I dig deeper to find out?

Many thanx in advance!

Lakehouses.jpg

 

1 ACCEPTED SOLUTION
GilbertQ
Super User
Super User

Hi @Alisea_MI 

 

You could use this from Michael at the Power BI Team to get a list of the Table and their sizes fabric_cat_tools/Model Optimization.ipynb at main · m-kovalsky/fabric_cat_tools · GitHub

 

And then use this in a notebook.

 

# Install Fabric CAT Tools
%pip install "https://raw.githubusercontent.com/m-kovalsky/fabric_cat_tools/main/fabric_cat_tools-0.3.0-py3-none-any.whl"

import fabric_cat_tools as fct
df = fct.get_lakehouse_tables(
        extended = True
        )

display(df)




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2 REPLIES 2
Alisea_MI
Resolver II
Resolver II

Many thanx, @GilbertQ ! I will definitely give it a try - unfortunately did not manage to find time this week.

GilbertQ
Super User
Super User

Hi @Alisea_MI 

 

You could use this from Michael at the Power BI Team to get a list of the Table and their sizes fabric_cat_tools/Model Optimization.ipynb at main · m-kovalsky/fabric_cat_tools · GitHub

 

And then use this in a notebook.

 

# Install Fabric CAT Tools
%pip install "https://raw.githubusercontent.com/m-kovalsky/fabric_cat_tools/main/fabric_cat_tools-0.3.0-py3-none-any.whl"

import fabric_cat_tools as fct
df = fct.get_lakehouse_tables(
        extended = True
        )

display(df)




Did I answer your question? Mark my post as a solution!

Proud to be a Super User!







Power BI Blog

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