Overview
A collection of data engineering projects focused on preparing data for analytics and reporting. The workflow covers data extraction, transformation, loading, database design, and the creation of structured warehouse tables.
Approach
Data is collected from relational database sources such as MySQL and PostgreSQL before being cleaned and transformed through ETL processes. Pentaho is used to orchestrate data extraction, transformation, validation, and loading into analytical structures.
The warehouse layer is designed to separate raw operational data from transformed analytical data. Appropriate tables, relationships, and fields are organized so downstream dashboards and analytical processes can work with consistent datasets.
Outcome
These projects demonstrate practical experience in building data pipelines and warehouse structures that support analytics. The resulting datasets are organized, reusable, and ready to be consumed by reporting tools, dashboards, and machine learning workflows.
