MarkPryceMaherMSFT

import json
import time
import struct
import sqlalchemy
import pyodbc
import notebookutils
import pandas as pd
from pyspark.sql import functions as fn
from datetime import datetime
import sempy.fabric as fabric

MarkPryceMaherMSFT

########################################################################################################
# Sample script to call the syncronisation between the Fabric Lakehouse and the SQL Endpoint
#        
## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#  This script is a workaround until the documented API is released: https://learn.microsoft.com/en-us/fabric/release-plan/data-warehouse#refresh-sql-analytics-endpoint-rest-api
#
#sempy version 0.4.0 or higher
!pip install semantic-link --q 
import json

MarkPryceMaherMSFT

MarkPryceMaherMSFT

MarkPryceMaherMSFT

create_new_table("control", False, False, False, False, False, False, False, False, False )
create_new_table("control", True, False, False, False, False, False, False, False, False )
create_new_table("control", False, True, False, False, False, False, False, False, False )
create_new_table("control", True, True, False, False, False, False, False, False, False )
create_new_table("control", False, False, True, False, False, False, False, False, False )
create_new_table("control", True, False, True, False, False, False, False, False, False )
create_new_table("control", False, True, True, False, False, False, False, False, False )
create_new_table("control", True, True, True, False, False, False, False, False, False )
create_new_table("control", False, False, False, True, False, False, False, False, False )
create_new_table("control", True, False, False, True, False, False, False, False, False )

MarkPryceMaherMSFT

def create_new_table(tablename, columnmapping,delete_vectors,partitions,columnmappingbyid,liquidclustering,optimize,vacuum,writeStatsAsStruct,writeStatsAsJson):
    # What the function does goes here
    # use the default sample table, publicholidays
    tablename = "PARQUET_2_0"
    
    if columnmapping==True and columnmappingbyid==False:
        tablename=tablename+"_columnmapping_by_name"
    if delete_vectors==True:
        tablename=tablename+"_delete_vectors"    
    if partitions==True:

MarkPryceMaherMSFT

MarkPryceMaherMSFT

# details from Article : https://learn.microsoft.com/en-us/fabric/data-warehouse/collation
# default collation is Latin1_General_100_BIN2_UTF8
# new collation is Latin1_General_100_CI_AS_KS_WS_SC_UTF8

#sempy version 0.4.0 or higher
!pip install semantic-link --q 
import json
import sempy.fabric as fabric
from sempy.fabric.exceptions import FabricHTTPException, WorkspaceNotFoundException

MarkPryceMaherMSFT

import pandas as pd
import struct
import sqlalchemy
import pyodbc
import notebookutils
import sempy.fabric as fabric
import time
from pyspark.sql import functions as fn
from datetime import datetime
def create_engine(connection_string : str):

MarkPryceMaherMSFT

import pandas as pd
import struct
import sqlalchemy
import pyodbc
import notebookutils
import sempy.fabric as fabric
import time
from pyspark.sql import functions as fn
from datetime import datetime
def create_engine(connection_string : str):