📅 2025-10-02 — Session: Developed YAML Structures for Data Management
🕒 18:35–20:25
🏷️ Labels: YAML, Data Management, Schema, Validation, EPH, HOGAR
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and develop YAML structures for various data management tasks, focusing on schema evolution, data mapping, and validation techniques.
Key Activities
- Reviewed the evolution of the INDIVIDUAL schema from 2010 to 2025, focusing on core elements and standardization strategies.
- Created a reference manual for changes in HOGAR and INDIVIDUAL data structures, including validations and normalization techniques.
- Explored the use of YAML for defining data structures in databases, emphasizing schema definitions and data validation.
- Developed a YAML metamodel for structuring EPH data, incorporating logical models and era-specific manifests.
- Provided a structured YAML configuration for EPH data mapping, detailing canonical columns and validation rules.
- Drafted a guide for YAML data ingestion and normalization, covering error handling and era detection.
- Structured YAML files for EPH and HOGAR systems, focusing on variable mapping and data configuration.
- Outlined YAML structures for household characteristics, survey design, and income metrics, providing templates for data management.
- Created a compact YAML structure for business data to enhance data management efficiency.
- Designed a declarative alignment profile in YAML for data processing with pandas, promoting reproducibility and clarity.
Achievements
- Successfully developed comprehensive YAML structures for various data management needs, enhancing standardization and validation processes.
- Provided detailed guides and templates for implementing YAML configurations across different data domains.
Pending Tasks
- Further testing and validation of the YAML structures in real-world scenarios to ensure robustness and adaptability.
- Integration of YAML configurations into existing data management systems for seamless operation.