Forum Discussion
Resolver ILoading an existing dataset/semantic model to Lakehouse
I have a dashboard that has been created by another department and I need to pull some of the data from it into our lake house. We are currently doing it manually where we export the data of a visual and import it in using a dataflow gen2.
I have been given contributor access to the other department workspace - however have not found a way to pull that data into my lake house. Any suggestions? I tried the dataflow connector but dont see the dataset in question in my list (it is a regular power bi dashboard importing data from a source and published to a premium workspace)
Adding what I did for reference-
- added a notebook with following code
# import fabric
from sempy import fabric as FabricDataFrame
# read the table
df_tables_ReqInfo = FabricDataFrame.read_table(workspace ="....." , dataset="....." , table="...")
# drop any extra columns
df_tables_OffersHireData.drop(['....', '.....'], axis = 1, inplace=True)
# write to lakehouse
df_tables_OffersHireData.to_lakehouse_table(name="....", mode="overwrite" )2. Created an enviroment to preload the semantic-link library and set it to as default for workspace - this is required if you want to call the notebook in a pipeline as pip is not allowed at that time.
6 Replies
- GilbertQ
Super User
Hi PowerNewUser
You can use a notebook to query data from another parvia semantic model and then load that into your lake house Here is an example. Fabric Semantic Link and Use Cases
PowerNewUserGilbertQ Seems to be promising - however am not experienced with the notebooks/PySpark - will try. Any other direct alternatives?
- PowerNewUser
Adding what I did for reference-
- added a notebook with following code
# import fabric
from sempy import fabric as FabricDataFrame
# read the table
df_tables_ReqInfo = FabricDataFrame.read_table(workspace ="....." , dataset="....." , table="...")
# drop any extra columns
df_tables_OffersHireData.drop(['....', '.....'], axis = 1, inplace=True)
# write to lakehouse
df_tables_OffersHireData.to_lakehouse_table(name="....", mode="overwrite" )2. Created an enviroment to preload the semantic-link library and set it to as default for workspace - this is required if you want to call the notebook in a pipeline as pip is not allowed at that time.
