ETL Process - ERP Data Analytics and Cloud Migration
This project aims to extract and analyze crucial information from a conventional ERP system, specifically Odoo, by querying its database. The objective involves extracting and storing relevant records into a cloud-based database service (AWS). Tasks include retrieving lists of clients, analytic accounts, contacts, products, production orders, and sales orders from the ERP. Additionally, it involves setting up a PostgreSQL database on AWS and integrating the extracted data into this new cloud database. The project utilizes Python, Pandas, SQLAlchemy, and Prefect to establish connections, perform data analysis, and manage the migration of information to the cloud.
This project tackles the challenge of efficiently extracting and analyzing critical data from a complex ERP system, specifically Odoo. By leveraging Python, Pandas, and SQLAlchemy, it aims to extract specific information such as client lists, analytic accounts, contacts, and sales/orders records from the ERP database. Additionally, the project involves migrating this extracted data to a cloud-based PostgreSQL database on AWS for improved scalability and accessibility. The objective is to streamline data extraction, analysis, and migration processes, enabling more effective business insights while harnessing the advantages of cloud infrastructure.
Problem
Traditional Enterprise Resource Planning (ERP) systems like Odoo often store extensive data, making it challenging to efficiently analyze and extract specific information for decision-making. These systems can be complex, and their native analysis tools might not meet the needs for in-depth business intelligence.
Solution
Traditional Enterprise Resource Planning (ERP) systems like Odoo often store extensive data, making it challenging to efficiently analyze and extract specific information for decision-making. These systems can be complex, and their native analysis tools might not meet the needs for in-depth business intelligence.
Conclusions
Enhanced Data Accessibility: The project successfully demonstrated the capability to extract and analyze targeted information from a complex ERP system like Odoo. This enhanced accessibility to specific data crucial for decision-making and business intelligence.
Improved Data Management: By utilizing Python, Pandas, and SQLAlchemy, the project streamlined data extraction, manipulation, and analysis processes. This improved data management facilitated a more efficient workflow for handling ERP data
Cloud Migration Benefits: The migration of extracted data to a cloud-based PostgreSQL database on AWS offered scalability and enhanced security. This shift to a cloud-based infrastructure potentially optimized data storage and accessibility.
Insight Generation: The project's success in migrating and analyzing data could lead to more informed business decisions. Insights gained from the ERP data could facilitate strategic planning, resource allocation, and better understanding of customer behavior.
Foundation for Further Development: Establishing the infrastructure and workflows for data extraction and migration sets a strong foundation for future development. It paves the way for ongoing data analysis, automation, and integration with other analytical tools or systems.
Technical Proficiency: The project likely bolstered the technical skills of the team involved, especially in Python, database querying, cloud infrastructure, and data management. This knowledge can be applied to future projects and endeavors.
© Nicolay Agustin. 2023