This project implements an end-to-end data migration pipeline for transferring enterprise data from Microsoft SQL Server to PostgreSQL using Python.
It was designed to simulate a real-world database migration scenario, focusing on data integrity, validation, automation, and reliability rather than simple one-time exports.
The pipeline handles schema replication, bulk data transfer, validation, and reporting through a structured Jupyter Notebook workflow.
In enterprise environments, database migrations are high-risk operations.
A failed migration can result in data loss, downtime, and business disruption.
This project was built to demonstrate how a production-style migration process can be implemented using open-source tools, with an emphasis on:
- Auditability
- Data quality
- Repeatability
- Validation
- Performance
- Transparency
Instead of manually exporting and importing tables, the pipeline automates the entire process and verifies that the target system is an accurate replica of the source.
The pipeline was designed around three core principles:
Every migration step is validated before and after execution:
- Baseline row counts are captured
- Data quality checks are performed
- Primary and foreign keys are verified
- Referential integrity is enforced
No table is considered successfully migrated until it passes validation.
All major tasks are automated:
- Schema extraction
- Type conversion
- Table creation
- Data loading
- Verification
- Reporting
This minimizes human error and enables repeatable executions.
The project mirrors real enterprise migration practices:
- Environment-based configuration
- Transaction management
- Rollbacks on failure
- Batched inserts
- Logging and reporting
The workflow is structured as a controlled ETL pipeline rather than a one-off script.
Before any data is moved, the pipeline audits the SQL Server database:
- Captures baseline row counts
- Detects null values in critical fields
- Identifies invalid email formats
- Finds orphaned foreign keys
- Flags future-dated records
This establishes a trusted reference point for validation.
The pipeline extracts metadata from SQL Server system catalogs and builds PostgreSQL-compatible schemas.
Key steps include:
- Reading column definitions
- Mapping SQL Server types to PostgreSQL types
- Preserving nullability constraints
- Reconstructing primary keys
- Creating target tables automatically
This removes the need for manual schema recreation.
During extraction, the data is normalized to match PostgreSQL conventions:
- Column names converted to lowercase
- Boolean fields normalized
- Type inconsistencies resolved
- Format adjustments applied
This ensures compatibility and consistency.
Data is loaded using bulk inserts with psycopg2.execute_values, which provides:
- Batched inserts
- Reduced network overhead
- Improved throughput
- Transaction safety
Target tables are truncated and reset before loading to avoid conflicts.
After loading, the pipeline validates the migration:
- Row counts compared with baseline
- Primary key uniqueness verified
- Foreign key relationships checked
- Data types inspected
Any mismatch is reported immediately.
The final stage generates validation reports and visualizations:
- Row count comparisons
- Log-scaled bar charts
- Summary validation output
This provides stakeholders with clear confirmation of migration success.
The project uses widely adopted, production-ready tools:
- Python 3 for orchestration
- pandas for data handling
- pyodbc for SQL Server connectivity
- psycopg2 for PostgreSQL access
- matplotlib for reporting
- python-dotenv for configuration management
- Jupyter Notebook for structured execution
This stack enables portability and maintainability.
Data-Migration-sql-server-to-postgres/ │
- ├── run_migration_uat.ipynb # Main migration workflow
- ├── generate_data.py # Synthetic data generator
- ├── .env # Environment configuration
- ├── .gitignore # Version control rules
- └── README.md # Documentation
The notebook acts as the main orchestration layer, while configuration is isolated through environment variables.
The pipeline follows a classic ETL model:
SQL Server → Extraction → Transformation → Loading → Validation → Reporting
Each phase is isolated, auditable, and restartable.
This design allows partial re-runs and controlled recovery in case of failure.
Instead of hardcoding schemas, the pipeline queries SQL Server metadata to dynamically generate PostgreSQL tables.
This makes the solution adaptable to new databases.
All credentials are stored in .env files to:
- Avoid hardcoding secrets
- Enable environment portability
- Support deployment pipelines
Using execute_values provides orders-of-magnitude performance improvement compared to row-by-row inserts.
This approach is essential for large datasets.
Validation is treated as part of the pipeline, not an afterthought.
This reflects real enterprise migration standards.
- Designed for batch migrations
- No real-time synchronization
- No change data capture (CDC)
- Assumes relatively stable schemas
These trade-offs were made to focus on correctness and reliability.
Potential extensions include:
- Incremental replication
- Workflow orchestration (Airflow/Prefect)
- Dockerized deployment
- Cloud-native migration support
- Automated CI/CD validation
This project demonstrates:
- Practical ETL pipeline design
- Database internals knowledge
- Data quality engineering
- Migration risk management
- Performance optimization
- Validation-driven development
It reflects production-oriented thinking rather than scripting.
Developed by Nte Daniels
Focus Areas:
Data Engineering | ETL Pipelines | Analytics | Database Systems
MIT License