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Project Duration
February 2025 - Ongoing
Client Industry
Saas and Tech
Target Markets
South Africa

Stride Forward Group

Complex SQL Server to PostgreSQL Migration with Referential Integrity and Safe Remigration Support

Technology Stack

No items found.

Client Overview

Challenges in Modeling Subscription and Business Metrics

Stride is a digital media management platform that helps music industry professionals organize and maintain detailed information about musicians, albums, and associated metadata. Their internal application centralizes artist-related data, streamlining operations for cataloging, updates, and reporting across music portfolios.

Solutions Delivered

Fully Automated Schema & Data Migration Pipeline

Foreign Key–Aware Loading with Insert Ordering

Deduplication Logic for Incremental Remigrations

Team Composition

Data Engineers

Python Developers

Database Migration Specialists

DevOps Support Engineers

Engagement Type

Contract on hourly basis

Key Challenges

Challenges in Migrating Complex Legacy SQL Server Data

Legacy Format & Limited Tooling

SQL Server’s .bak format restricted the use of standard ETL pipelines and required custom extraction logic.


Tangled Schema Dependencies

Dozens of foreign-key–linked tables introduced risk of referential mismatches during migration.


Deduplicated Migration

Client required re-runnable, partial migrations without duplicate insertions or data corruption.


Strategic Roadmap

The project began with schema introspection from the SQL Server .bak backup, reconstructing PostgreSQL-compatible DDL using Python scripts. Pandas DataFrames were used to extract, inspect, and transform table data for loading.A two-phase migration plan was adopted—first loading dimension tables, capturing their new primary keys, and then loading dependent fact tables by resolving foreign keys dynamically. The system included row-level comparison logic to ensure that any remigrations were idempotent and skipped already-migrated records.

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To tackle complex legacy migrations, we built Python-based schema introspection scripts that dynamically extracted SQL Server structures and generated PostgreSQL-compatible DDLs. Data was processed in-memory using Pandas, allowing precise row-level transformations and type casting.

Execution Approach

Robust, Incremental Migration Framework for Legacy SQL Server Data

To tackle complex legacy migrations, we built Python-based schema introspection scripts that dynamically extracted SQL Server structures and generated PostgreSQL-compatible DDLs. Data was processed in-memory using Pandas, allowing precise row-level transformations and type casting.
The migration framework included deduplication and delta detection, ensuring only new or changed records were inserted.
Automated insert sequencing honored referential integrity by managing primary and foreign key dependencies in memory. This approach enabled safe, incremental migrations without data corruption or duplication, ensuring seamless and repeatable migration across interdependent tables.

BUSINESS IMPACT

Zero-Error Migration with Referential Integrity
Repeatable, Idempotent Loads Enabled
Modernized Data Infrastructure
Improved Data Access and Analytics

100%

foreign key integrity preserved post-migration


30+

interrelated tables migrated with no manual inserts required


0

duplicate records after multiple test retruns