Real Data Pipeline (SQL + Python)
In this project-based tutorial, we'll build a complete data pipeline using SQL Server, Python, Pandas, and pyodbc.
Starting with a raw SQL table, we'll pull the data into Python, perform data quality checks, clean and standardize fields, identify duplicate transactions, flag problematic records, and create a clean reporting dataset. We'll then aggregate the data into a weekly reporting table and load the results back into SQL Server.
In this video you'll learn:
• Connect Python to SQL Server using pyodbc
• Read SQL data into Pandas
• Inspect and profile a dataset
• Remove whitespace and standardize text fields
• Validate business rules and identify bad records
• Detect duplicate transactions
• Create data quality flags
• Build a clean daily dataset
• Create a weekly reporting table using SQL
• Load cleaned data back into SQL Server
This is a realistic workflow that combines SQL and Python to transform raw data into analysis-ready reporting tables.
Tools Used:
SQL Server
Python
Pandas
pyodbc
Dataset
https://github.com/Gaelim/youtube/blo...
#Python #SQL #SQLServer #Pandas #DataAnalytics #DataEngineering #DataPipeline #DataCleaning
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