enterprise-data-engineering-pipeline-ssis-pyspark

Enterprise Data Engineering Pipeline (SSIS + PySpark) is a reference skill documenting an end-to-end ELT solution for enterprise data warehousing and analytics. It solves the problem of assembling a full pipeline from disparate Microsoft and big-data tools by combining SSIS for ETL orchestration, SQL Server with a star-schema data warehouse, Python and Pandas for data-quality audits and visualization, and PySpark for large-scale analytics and aggregation. The pipeline ingests raw CSV files (Sales, Products, Customers), transforms them through SSIS, loads them into a dimensional model, and performs analytics at scale.

The documentation lays out the architecture in layers — a source layer of raw CSVs, an ETL layer of SSIS packages handling extraction, transformation, and error handling, a SQL Server storage layer with fact and dimension tables, and an analytics layer of Python and PySpark scripts. It provides prerequisites and setup (SQL Server 2019+, SSIS, SSDT, Python 3.10+, Java 8+ for PySpark), the pip dependencies, and SQL DDL that creates the warehouse database, dimension and fact tables, and business-intelligence views such as revenue by product and customer lifetime value.

It targets enterprise data engineers and BI developers working in the Microsoft data stack who need a worked blueprint for a star-schema warehouse fed by SSIS and analyzed with PySpark. As a documentation-and-reference skill describing standard warehouse setup, DDL, and analytics scripts with no destructive or exfiltration behavior, it is benign educational tooling.