Data Warehouse Modernization

Led migration from Oracle to PostgreSQL, enhanced ETL orchestration & implemented CI/CD, resulting in 100% cost reduction and 20% performance improvement

Context

A company was facing high costs and suboptimal performance with its Oracle-based data warehousing infrastructure. Data layer management using Python scripts was complex and lacked automation and robustness

Project Objectives

• Fully migrate the enterprise data warehouse (DWH) from Oracle to PostgreSQL to improve performance, scalability and cost efficiency.
• Optimize and modernize the existing data layer, previously managed with Python scripts.
• Implement a more efficient orchestration system for ETL processes.
• Introduce Continuous Integration/Continuous Deployment (CI/CD) practices for data layer management.
• Improve the overall software infrastructure supporting the data warehouse.

Implemented Solution

I led a comprehensive data warehouse modernization initiative, addressing both database migration and improvement of the data layer and surrounding software infrastructure. Key activities included:

Key Steps

• Database migration from Oracle to PostgreSQL, optimizing data structures for the new environment.
• Review and optimization of existing Python scripts for data layer management.
• Implementation of an orchestration system (Apache Airflow) to automate and monitor ETL workflows.
• Creation of CI/CD pipelines to automate testing and deployment of changes to the data layer and ETL processes.
• Improving software infrastructure, including adoption of version control practices (Git) and standardization of development and production environments.
• Immproving query performacnes by leveraging indexes and partitioning large tables
• Ensuring data integrity and business continuity throughout the transition process, coordinating closely with stakeholders.

Skills Used

Data migration, PostgreSQL optimization, ETL redesign and orchestration, Python development, CI/CD implementation, software infrastructure improvement, version control (Git), stakeholder management, problem-solving, strategic planning.

Outcomes

• DWH migration successfully completed, with a 100% reduction in database-related costs.
• Improved ETL and query processing speed by 20%, enabling faster data availability for reporting.
• Modernization of the data layer with an efficient and automated orchestration system.
• Introduction of CI/CD practices that improved stability, reliability, and speed of deployment of changes to the data warehouse.
• Provision of a more robust, scalable and easy-to-maintain software infrastructure to support future data analysis needs.