Disclaimer: This blog post was created with the assistance of AI. While the content reflects my personal experiences and insights, the AI was used to enhance the writing process and ensure clarity.
In my role as a software engineer at CGI, I had the unique opportunity to work on a Proof of Concept (POC) that involved migrating a robust Software Delivery Platform for a prominent telecommunications client. The goal was to transition this platform from GraphDB to PostgreSQL and build an improved interface using Odoo, all while preserving data integrity and enhancing the user experience. This project brought to light the complexity and rigor required in a large-scale data migration, particularly one that involved around 20 million records.
Project Background and Key Migration Objectives
The client’s platform originally leveraged GraphDB as its database solution, but the plan for migration aimed to restructure this system within Odoo and adopt PostgreSQL as the new database backbone. The transition was far from straightforward; it involved converting a graph-based database structure to a relational one while ensuring accuracy, consistency, and compatibility with the new environment.
Migration Process: Steps, Challenges, and Solutions
Migrating a high-volume, graph-based dataset to a relational database required a comprehensive and structured approach. Here’s how we tackled the migration:
Data Analysis and Mapping
Our first step was an in-depth analysis of the existing GraphDB data schema. GraphDB’s interconnected data structure does not easily translate to a relational format, so this analysis was crucial to determine how the data could best be organized within PostgreSQL. I developed data mapping sheets to identify relationships, dependencies, and areas requiring transformation. These mapping sheets became an essential reference throughout the migration, guiding each stage of data processing and helping avoid potential pitfalls.
Data Cleansing and Preparation
Migrating legacy data presents challenges around consistency, accuracy, and relevance. We undertook a thorough cleansing process to identify and remove redundant or obsolete records, standardize values, and structure data for the new format. With around 20 million records involved, this phase was both meticulous and time-intensive. The cleansing not only ensured accuracy but also minimized migration risks, reducing the chances of data conflicts during integration.
Database Structuring and Transformation
The graph database’s inherently linked nodes and edges had to be restructured into tables and relationships within PostgreSQL. Here, I wrote python scripts to convert the graph-based relationships into a format PostgreSQL could process efficiently, creating tables, primary keys, and foreign key constraints that would support the platform’s functionality in Odoo. This restructuring maintained the core relationships but in a way that a relational database could handle effectively.
Data Transfer and Testing
With PostgreSQL schema set up and data cleansed, we proceeded to the data transfer phase. Transferring 20 million records is a complex task, so I implemented batch migration processes to avoid overwhelming the server. I had developed a pipeline which used GitLab webhooks that interacted with the PostgreSQL database. using Each data transfer session was followed by integrity checks and thorough testing within PostgreSQL to confirm the accuracy of relationships and data values. This iterative approach allowed us to catch and resolve inconsistencies early on, maintaining a high standard of data accuracy.
Validation in Odoo
Once data was successfully transferred, we moved to validate it within Odoo. This step involved connecting the PostgreSQL database to Odoo and configuring the interface to reflect the client's specific processes and workflows. The migrated data was then tested for performance, accuracy, and completeness within Odoo, allowing us to make final adjustments before deploying it onto a Linux server.
Impact of the Migration
The successful migration of the platform’s data to PostgreSQL resulted in a more consistent and organized database structure, enabling smoother operations within Odoo. The new relational format in PostgreSQL improved query efficiency, contributing to faster response times for users. Additionally, the system’s enhanced data integrity and reliability made the platform more adaptable for future development and scaling, ultimately benefiting both the client and its end customers.
Key Takeaways
This project reinforced the importance of a systematic, step-by-step approach to data migration. Careful mapping, cleansing, and transformation of legacy data ensured that the move from GraphDB to PostgreSQL went smoothly. Each phase, from data analysis to testing, highlighted the need for accuracy, foresight, and flexibility in handling complex data structures.
For anyone facing a similar migration challenge, taking the time to plan each phase, anticipate data transformation requirements, and implement rigorous validation steps is essential for a successful outcome. This experience underscored the potential of using PostgreSQL with Odoo for efficient, scalable software delivery platforms and contributed valuable insights to my skillset as a software engineer.
Data migration of this scale is no small feat, and I’m thrilled to have been part of a project that merged technical innovation with practical impact. The migration taught me invaluable lessons in data handling and structured transformation—skills I’m excited to bring into future projects.