Category Archives: Table Variable TSQL

Merry Christmas Data Classification, Feature Engineering , Data Governance. ‘How to’ do it and some code take a look

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I was heavily involved in business intelligence, data warehousing and data governance as of several years ago and recently have had many chaotic personal challenges, upon returning to professional practice I have discovered things have not changed that much in 10 yearsagovernance The methodologies and approaches are still relatively consistent however the tools and techniques have changed and In my opinion not for the better, without focusing on specific tools I’ve observed that the core to data or MDM is enabling and providing a capability for classifying data into business categories or nomenclature.. and it has really not improved.

  • This basic traditional approach has not changed, in essence man AI model predicst a Metric and is wholly based on the integrity of its features or Dimensions.



Therefore I decided, to update some of the techniques and code patterns, I’ve used in the past regarding the information value chain and or record linkage , and we are going to make the results available with associated business and code examples initially with SQL Server and data bricks plus python

My good friend, Jordan Martz of DataMartz fame has greatly contrinuted to this old mans BigData enlightenment as well as Craig Campbell in updating some of the basic classification capabilities required and critical for data governance. If you would like a more detailed version of the source as well as the test data, please send me an email at iwhiteside@msn.com. Stay tuned for more update and soon we will add Neural Network capability for additional automation of “Governance Type” automated classification and confidence monitoring.

Before we focus on functionality let’s focus on methodology


Initially understand key metrics to be measured/KPI‘s their formulas and of course teh businesse’s expectation of their calculations

Immediately gather file sources and complete profiling as specified in my original article found here

Implementing the processes in my meta-data mart article would provide numerous statistics regarding integers or float field however there are some special considerations for text fields or smart codes

Before beginning classification you would employ similarity matching or fuzzy matching as described here



As I said I posted the code for this process on SQL Server Central 10 years ago here is s Python Version.

databricks-logo Roll You Own – Python Jaro_Winkler(Python)

databricks-logoroll You Own – Python Jaro_Winkler(Python)
Import Notebook
Step 1a – import pandas
import pandas

Step 2 – Import Libraries

libraries from pyspark.sql.functions import input_file_name

from pyspark.sql.types import *
import datetime, time, re, os, pandas

ML Libraires

from pyspark.ml.feature import RegexTokenizer, StopWordsRemover, NGram, HashingTF, IDF, Word2Vec, Normalizer, Imputer, VectorAssembler
from pyspark.ml import Pipeline

import mlflow

from mlflow.tracking import MLFlowClient

from sklearn.cluster import KMeans
import numpy as np
Step 3 – Test JaroWinkler

JaroWinkler(‘TRAC’,’TRACE’)
Out[5]: 0.933333
Step 4a =Implement JaroWinkler(Fuzzy Matching)

%python

def JaroWinkler(str1_in, str2_in):

if(str1_in is None or str2_in is None):
return 0

tr=0
common=0
jaro_value=0
len_str1=len(str1_in)
len_str2=len(str2_in)
column_names=[‘FID’,’FStatus’]

df_temp_table1 = pandas.DataFrame(columns=column_names)
df_temp_table2 = pandas.DataFrame(columns=column_names)

#clean_string(str1_in)
#clean_string(str2_in)

if len_str1 > len_str2:
swap_len=len_str2
len_str2=len_str1
len_str1=swap_len
swap_str=str1_in
str1_in=str2_in
str2_in=swap_str

max_len=len_str2

iCounter=1

while(iCounter <= len_str1):
df=pandas.DataFrame([[iCounter,0]], columns=column_names)
df_temp_table1=pandas.concat([df_temp_table1,df], ignore_index=True)
iCounter=iCounter+1

iCounter=1

while (iCounter <= len_str2):
df=pandas.DataFrame([[iCounter,0]], columns=column_names)
df_temp_table2=pandas.concat([df_temp_table2,df], ignore_index=True)
iCounter=iCounter+1

iCounter=1
m=round((max_len/2)-1,0)
i=1

while(i <= len_str1):
a1=str1_in[i-1]

if m >= i:
  f=1
  z=i+m 
else:
  f=i-m
  z=i+m 
if z > max_len:
  z=max_len
while (f <= z):
  a2=str2_in[int(f-1)]
  if(a2==a1 and df_temp_table2.loc[f-1].at['FStatus']==0):
    common = common + 1
    df_temp_table1.at[i-1,'FStatus']=1
    df_temp_table2.at[f-1,'FStatus']=1 
    break
  f=f+1
i=i+1

i=1
z=1
while(i <= len_str1): v1Status=df_temp_table1.loc[i-1].at[‘FStatus’] if(v1Status==1): while(z <= len_str2): v2Status=df_temp_table2.loc[z-1].at[‘FStatus’] if(v2Status==1): a1=str1_in[i-1] a2=str2_in[z-1] z=z+1 if(a1 != a2 ): tr = tr+0.5 break break i=i+1 wcd = 1.0/3.0 wrd = 1.0/3.0 wtr = 1.0/3.0 if (common != 0): jaro_value = (wcd * common)/ len_str1 + (wrd * common) / len_str2 + (wtr * (common – tr)) / common return round(jaro_value,6) Step 4b – Register JaroWinkler spark.udf.register(“JaroWinkler”, JaroWinkler) Out[6]:
Step8a – Bridge vs Master vs AssociativeALL

%sql
DROP TABLE IF EXISTS NameAssociative;
CREATE TABLE NameAssociative;

SELECT
Name
,NameInput

,sha2(replace ( NameLookput,’%[^a-Z0-9, ]%’,’ ‘) , 256) as NameLookupCeaned ,a.NameLookupKey
,sha2(replace( NameInput,’%[^a-Z0-9, ]%’,’ ‘) , 256) as NameInput,b.NameInputKey
,JaroWinkler(a.NameLookup, b.NameInput) MatchScor
,RANK() OVER (Partition by a.DetailedBUMaster ORDER BY JaroWinkler(a.NameLookupCleande, b.NameInputCleaned) DESC) NameLookup,b.NameLookupKey
FROM NameInput as a
CROSS JOIN NameLookup as b

Guerilla MDM via Microsoft MDS: Force Model Validation or TSQL Script to force set Validation Status ID ‘s

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Using Metadata and code generation to programmatically set Validation Status ID’s  for all leaf members in an MDS Entity.

Primarily this script relies on the MDS mdm.udpMemberValidationStatusUpdate procedure for a single member. Unfortunately I could not get the stored proc for multiple members(mdm.udpMembersValidationStatusUpdate) to work, so I created a script to rely on the single member version.( mdm.udpMemberValidationStatusUpdate). I could not resolve the “operand type clash” error.

The script requires the model name and entity name, to generate a TSQL statement to update each member id to the designated Validation status.

IF OBJECT_ID('tempdb..#MemberIdList') IS NOT NULL
DROP TABLE #MemberIdList

DECLARE @ModelName nVarchar(50) = ‘Supplier’
DECLARE @Model_id int
DECLARE @Version_ID int
DECLARE @Entity_ID int
DECLARE @Entity_Name nVarchar(50) = ‘Supplier’
DECLARE @Entity_Table nVarchar(50)
DECLARE @sql nVarchar(500)
DECLARE @sqlExec nVarchar(500) = ‘EXEC mdm.udpMemberValidationStatusUpdate ‘
DECLARE @ValidationStatus_ID int = 4
— Found the following information intable [mdm].[tblList]
–ListCode ListName Seq ListOption
–lstValidationStatus ValidationStatus 0 New, Awaiting Validation
–lstValidationStatus ValidationStatus 1 Validating
–lstValidationStatus ValidationStatus 4 Validation Failed
–lstValidationStatus ValidationStatus 3 Validation Succeeded
–lstValidationStatus ValidationStatus 2 Awaiting Revalidation
–lstValidationStatus ValidationStatus 5 Awaiting Dependent Member Revalidation
–MemberType_ID = 1 (Leaf Member)
DECLARE @MemberType_ID int = 1
–Get Version id for Model
SET @Version_ID = (SELECT MAX(ID)
FROM mdm.viw_SYSTEM_SCHEMA_VERSION
WHERE Model_Name = @ModelName)
print @Version_ID
–Get Model IDfor Model
SET @Model_ID = (SELECT Model_ID
FROM mdm.viw_SYSTEM_SCHEMA_VERSION
WHERE Model_Name = @ModelName)
print @Model_ID
–Get Entity ID for specific Entity
SET @Entity_ID =
(SELECT [ID]
FROM [mdm].[tblEntity]
where [Model_ID] = @Model_ID
and [Name]= @Entity_Name)

–Get Entity Table Namefor specific Entity

SET @Entity_Table =
(SELECT[EntityTable]
FROM [mdm].[tblEntity]
where [Model_ID] = @Model_ID
and [Name]= @Entity_Name)
print ‘Processing Following Model ‘ + convert(varchar,@ModelName)
print ‘Model ID = ‘ + convert(varchar,@Model_id)
print ‘Version ID = ‘ + convert(varchar,@Version_ID)
print ‘Entity = ID ‘ + convert(varchar,@Entity_ID)
print ‘Entity Name = ‘ + convert(varchar,@Entity_Name)
print ‘Entity Table Name ‘ + convert(varchar,@Entity_Table)
print ‘Validation Status ID being set to ‘ + convert(varchar,@ValidationStatus_ID) + ‘ for all members in ‘+ convert(varchar,@Entity_Name)

–Create local temp table to hold member ids to update
CREATE TABLE #MemberIdList
( id int,
Processed int)
–Generate SQL to populaet temp table
set @sql = N’INSERT INTO #MemberIdList select id, 0 from mdm.’ +convert(varchar,@Entity_Table)

–Generate SQL to display stored proc update
EXECUTE sp_executesql @sql

–Create Tabe Variable to hold SQL Commands and prepare for execute
Declare @Id int
DECLARE @MemberSQL TABLE
(
ID int,
mdssql nvarchar(500),
Processed int
)
While (Select Count(*) From #MemberIdList Where Processed = 0) > 0
Begin
Select Top 1 @Id = ID From #MemberIdList Where Processed = 0

set @sql = N’ mdm.udpMemberValidationStatusUpdate ‘ +convert(varchar,@Version_ID) +’,’ +convert(varchar, @Entity_ID) +’,’ +convert(varchar,@ID) +’,’ +convert(varchar, @MemberType_ID) +’,’ + convert(varchar,@ValidationStatus_ID)

print @sql

INSERT INTO @MemberSQL
Select @id , @sql, 0

Update #MemberIdList Set Processed = 1 Where ID = @Id

End

Declare @sqlsyntax nVarchar(500)

While (Select Count(*) From @MemberSQL Where Processed = 0) > 0
Begin
Select Top 1 @id = id From @MemberSQL Where Processed = 0
Select Top 1 @sqlsyntax = mdssql From @MemberSQL Where Processed = 0

print @sqlsyntax
— comment this line to not execute update Validation Status ID
EXECUTE sp_executesql @sqlsyntax

Update @MemberSQL Set Processed = 1 Where ID = @Id
End

Declare @Entity_TableSQL nVarchar(500)
set @Entity_TableSQL = ‘SELECT * FROM [mdm].’ + @Entity_Table
print @Entity_TableSQL
exec sp_executesql @Entity_TableSQL
Here are several links I referenced:

The code for forcing the Validation of the Model is here. Jeremey Kashel’s Blog

Microsoft SQL Server 2012 Master Data Services 2/E Tyler Graham

Table Variables in TSQL

I am sure this can be improved, please contact me with questions or suggestions.

-Ira Warren Whiteside