Designed ETL and Data Processing Frameworks

📅 2025-09-04 — Session: Designed ETL and Data Processing Frameworks

🕒 21:35–23:00
🏷️ Labels: ETL, Data Processing, Architecture, Modular Design, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

The session aimed to design and outline frameworks for ETL and data processing systems, focusing on modular, evergreen, and decoupled architectures.

Proposed a mapping of playbooks and clusters to improve data management, including corrections and missing IDs.
Outlined a Jupyter notebook for ETL workflows related to poverty metrics, covering environment setup, data preprocessing, and QA visualization.
Reflected on ETL flows for data transformation from household surveys and census data, considering robustness and scalability.
Planned the transformation of traditional ETL systems into evergreen systems, emphasizing automation and data governance.
Developed a high-level overview of a decoupled production architecture, detailing repositories, orchestration, and CI/CD processes.
Designed a modular architecture for data processing and machine learning, focusing on extensibility and evergreen lifecycle.
Described tools for poverty research in Argentina, including eph-extractor, censo-sampler, poverty-etl, and poverty-ml.

Established a comprehensive framework for ETL and data processing, integrating modern practices like modular design and evergreen systems.
Enhanced the strategic direction for data management and processing, aligning with personal branding efforts in the data science domain.

Implementation of the proposed ETL and data processing frameworks.
Further exploration of automation and governance strategies for evergreen systems.