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SQL Server DB Python Builder

Project completed in 7 times less weeks than originally estimated (86% time reduction)

📅

Before: Weeks
(Manual tedious work)

→
⚡

Now: Seconds!
(Error-free automation)

💡

Focus here is functionality, not coding practices.

These projects were built over a year ago with the sole purpose of solving real business problems efficiently. They demonstrate my ability to identify repetitive tasks and automate them effectively. The goal was not to create a perfect project, but a fast solution. Do not evaluate my coding standards or practices based on them.

Project Context

🏢
Setting

Data-specialized consultancy in Spain

🎓
Client

One of Spain's most prestigious universities

📊
The Challenge

Build database structure with 350+ tables in allocated timeframe

💡
My Approach

Automated the entire process with Python instead of manual creation

⚡
Development

Few days to build
the automation script

🚀
Task Impact

7x faster completion than
allocated project timeline
86% time reduction

🔄
Future Value

Future changes done in seconds
compared to weeks of manual work

🗄️ About the Database Structure

⚠️ The Challenge

Build a new database structure with 350+ tables that needed to be created manually, one by one, across 4 different layers:

1. SSIS Packages

Extract data from Oracle to SQL Server

2. STG Layer

SQL Server First layer: staging tables

3. ODS Layer

SQL Server Second layer: operational data store

4. Stored Procedures

Data transformations STG → ODS

Database Migration Architecture
graph TD subgraph "Oracle Source Systems" A[ORIGIN_1 Database<br/>📊 Tables & Data] B[ORIGIN_2 Database<br/>📊 Tables & Data] end subgraph "Python Automation Engine" C[📋 Data Dictionary<br/>350+ Tables Metadata] D[🐍 Python Builder<br/>Auto-generation Script] end subgraph "Generated Components" E[📦 SSIS Packages<br/>XML Code Generation] F[🗄️ SQL Server STG<br/>Staging Layer] G[🗄️ SQL Server ODS<br/>Operational Data Store] H[⚙️ Stored Procedures<br/>ETL Transformations] end A --> E B --> E C --> D D --> E D --> F D --> G D --> H E --> F F --> H H --> G classDef oracle fill:#ff9999,stroke:#ff3333,stroke-width:2px classDef python fill:#4CAF50,stroke:#2E7D32,stroke-width:2px classDef generated fill:#2196F3,stroke:#1565C0,stroke-width:2px class A,B oracle class C,D python class E,F,G,H generated
⏰ Manual Time Requirements

350+ tables × 20 columns average = 7,000+ individual elements to create manually across 4 layers:

Per Table Estimates: (Work to do for each one of the +350 tables)
  • • SSIS Package: 45-60 minutes
  • • STG Table: 15-20 minutes
  • • ODS Table: 15-20 minutes
  • • Stored Procedure: 30-45 minutes

Total per table: ~2 hours

⚠️ Additional Manual Process Challenges:
  • • Column name variations: Many tables have different naming conventions requiring manual checking
  • • Data type mismatches: Oracle to SQL Server type conversions need individual verification
  • • Human error risk: Repetitive manual tasks are highly prone to mistakes and inconsistencies
  • • Version control issues: Manual changes across 350+ tables create maintenance nightmares
🚀 The Solution Impact

My Python automation eliminated this extremely tedious and time consuming manual process. Instead of creating each table manually across all layers, the script generates everything automatically: SSIS packages with complex XML structures, SQL table definitions, and stored procedures with transformations—all in seconds rather than weeks, with zero human error risk.

🔧 Technical Challenges & Achievements

1. Complex SSIS Package Generation
  • • Programmatically created SSIS packages for 350+ tables
  • • Each package containing hundreds of lines of XML code
  • • Automated weeks of manual effort into seconds of execution
  • • Ensured precise XML structure adherence for SSIS functionality
2. Multi-layered Database Structure
  • • Managed table creation across multiple database layers (STG, ODS)
  • • Maintained data integrity and consistency across layers
  • • Implemented complex transformations between layers
3. Cross-Database Compatibility
  • • Translated Oracle-specific features to SQL Server equivalents
  • • Handled different data types and constraints across platforms
  • • Ensured data integrity across different database systems

📂 GitHub Repository: View Complete Source Code →

📩 Ready for Your Own Transformation?

By leveraging technology to solve real-world problems, I help clients optimize their operations and focus on what truly matters.

💬 Interested in a similar transformation?

Let's chat about how I can tailor a solution for your specific needs!

⚠️ Confidentiality: For confidentiality reasons, I can't provide the university name or exact time details, but both can be fact-checked during the recruitment process.