Enhanced Data Transparency in Merging Datasets

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📅 2023-08-17 — Session: Enhanced Data Transparency in Merging Datasets

🕒 18:15–19:05
🏷️ Labels: Data Processing, DBML, Schema Design, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to enhance data transparency and verification during the data merging process and to refine the database schema for population and radio data.

Key Activities

  • Modified geo_personas Function: Implemented a loop to print columns and their maximum values in datasets before merging, facilitating better data transparency.
  • Added Print Statements: Enhanced data verification by displaying maximum values of columns in datasets prior to merging.
  • DBML Schema Definition: Proposed and updated the database schema using DBML to define and refine the structure and relationships of tables related to population data and radio circuits.
  • Schema Updates: Revised the database schema by removing the radios_circuitos table and detailing the relationships between the remaining tables.

Achievements

  • Successfully modified the geo_personas function to include data transparency features.
  • Developed a comprehensive DBML schema that clearly defines the database relationships, enhancing data modeling and integrity.

Pending Tasks

  • Further testing of the modified merging process to ensure accuracy and efficiency.
  • Continuous refinement of the database schema as new data requirements emerge.

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