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enterprise-data-engineering-pipeline-ssis-pyspark — Claude Code skill

Guides coding assistants through building an ELT pipeline with SQL Server star schemas, SSIS orchestration packages, and PySpark analytics jobs.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add reason-machines/data-skills --skill enterprise-data-engineering-pipeline-ssis-pyspark -a claude-code
Open on GitHub

What enterprise-data-engineering-pipeline-ssis-pyspark does

This skill structures an end-to-end data platform around Microsoft SQL Server and Python tools. Raw data from flat files lands into an EnterpriseDataWarehouse database through SQL Server Integration Services. Processing follows clear boundaries across layers.

The design centers on a star schema with dim_Customers, dim_Products, and fact_Sales tables alongside views for customer lifetime value. Python scripts handle data validation: project_audit.py scans tables for null entries and orphaned records using SQLAlchemy. PySpark handles downstream aggregations when record counts grow large.

Local execution requires SQL Server 2019 or newer, Visual Studio with SSDT, Python 3.10+, and Java 8+ to support PySpark. Missing any of these dependencies breaks the workflow immediately.

What it helps with

  • deploy a SQL Server star schema with facts and dimensions
  • build SSIS packages to extract and load flat file records
  • run automated data quality checks for orphaned records and missing values
  • compute customer lifetime value and product metrics via SQL views

Example prompts for the agent

  • Help me configure an SSIS package to load Sales.csv into fact_Sales
  • Generate the SQL Server star schema for customer and product dimensions
  • Run data quality checks on EnterpriseDataWarehouse using project_audit.py

How to install enterprise-data-engineering-pipeline-ssis-pyspark

1Skills CLI (Vercel)

npx skills add reason-machines/data-skills --skill enterprise-data-engineering-pipeline-ssis-pyspark -a claude-code

2Manually: put the files in place

~/.claude/skills/enterprise-data-engineering-pipeline-ssis-pyspark/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/enterprise-data-engineering-pipeline-ssis-pyspark/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about enterprise-data-engineering-pipeline-ssis-pyspark

How do I install enterprise-data-engineering-pipeline-ssis-pyspark in Claude Code?

Run npx skills add reason-machines/data-skills --skill enterprise-data-engineering-pipeline-ssis-pyspark -a claude-code in your terminal.

What does this skill provide?

It provides architectural guidance and code templates for building an on-premise data warehouse. The content includes SQL table schemas, SSIS task structures, and audit routines in Python.

What software is required to run the pipeline?

You need SQL Server 2019 or later, Visual Studio with SSDT, Python 3.10+, and Java 8+ for PySpark execution.

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