Forum Discussion
Resolver ILoading an existing dataset/semantic model to Lakehouse
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.
Super UserHi 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.
Luci7Helper I
PowerNewUser Thank you so much for posting the solution. I was trying to find a way and you did it! I'll add a link to your solution in the post I created.