enterprise-data-engineering-pipeline-ssis-pyspark
Turn raw sales data into a warehouse with quality checks.
What it does
Builds a dimensional data warehouse from CSV data, with ETL orchestration, data quality checks, and large-scale analytics.
When to use it
Use when setting up enterprise ETL, designing a star-schema warehouse, auditing warehouse data, or scaling analytics with PySpark.
How to use it
Give it sales, product, and customer CSV files; it builds a warehouse pipeline with an SSIS project and Python/PySpark analytics.
What you provide
- Your files
Uses
Good to know
- cannot be undone: SQL Server
What you need · 10
Requires SQL Server 2019 or later, Developer or Enterprise Edition.
Requires SSIS for ETL orchestration and package execution.
Access · 2
This skill
SQL Server
Loads data and overwrites analytics (irreversible)
pip install pandas sqlalchemy pyodbc pyspark matplotlib
Installs Python analytics dependencies