I have need to compare amount rows of Azure SQL databases to check data quality. There are about 50 database tables and occasionally new tables are added. I would like to build with Python script that connects to 3 environments and build reports if tables are ok.

I am wondering where to store results with Python during data comparing process. And I am wondering if comparison should be done 2 tables in once or all 3 in once.

Example of report:

"", "Dev","Test","Prod"
"Table A", "120","120","120"
"Table B", "150","150","50"

Let say that I would like to execute this statement to each DB, what architecture to compare values and create reports?

Possibly this query can be used.

SELECT (SCHEMA_NAME(A.schema_id) + '.' + A.Name) AS TableName  
, SUM(B.rows) AS RecordCount  
FROM sys.objects A  
INNER JOIN sys.partitions B ON A.object_id = B.object_id  
WHERE A.type = 'U'  
GROUP BY A.schema_id, A.Name

Python code for fetching dev:

import pyodbc 
 import pandas as pd

 conn = pyodbc.connect('Driver={SQL Server};'
                  'Server=server_name;'
                  'Database=database_name;'
                  'Trusted_Connection=yes;')

 cursor = conn.cursor()
 sqlquery = "SELECT (SCHEMA_NAME(A.schema_id) + '.' + A.Name) AS 
 TableName  , SUM(B.rows) AS RecordCount FROM sys.objects A  INNER JOIN 
 sys.partitions B ON A.object_id = B.object_id  WHERE A.type = 'U' GROUP 
 BY A.schema_id, A.Name"

 df = pd.read_sql_query(sqlquery, conn)

 print(df)
 print(type(df))

Results from Dev

TableName  RecordCount
0    TableA         120
1    TableB         150

What can I try next?