Designed ETL and Data Processing Frameworks

• Day: 2025-09-04
• Time: 21:35 to 23:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: ETL, Data Processing, Architecture, Modular Design, Machine Learning

Description

Session Goal:

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

Key Activities:

• 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.

Achievements:

• 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.

Pending Tasks:

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

Evidence

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• event_ids: []