Refactored Data Pipeline and Implemented Smoke Testing

• Day: 2026-03-10
• Time: 09:10 to 09:40
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Pipeline, Orchestration, Smoke Testing, Migration, Python

Description

Session Goal

The session aimed to refactor the data pipeline for Stage 01, implement orchestration for prediction processes, and introduce smoke testing to ensure pipeline integrity.

Key Activities

• Loading and Inspecting Jupyter Notebooks: Utilized Python’s JSON module to load and inspect Jupyter notebooks, focusing on cell structure and content.
• Data Processing and Model Prediction: Executed scripts for data processing and model prediction, specifically targeting census data.
• Migration Strategy for Predictive Module: Outlined a strategy to transform Stage 01 into a callable module, including orchestration logic migration and file restructuring.
• Orchestration Implementation: Developed an orchestration layer for Stage 01, defining key functions for processing labor-market inputs.
• Smoke Runner Implementation: Implemented a smoke runner in run_smoke.py to dispatch stages conservatively and handle errors.
• Migration Memo for IP-UBA Pipeline: Documented the migration strategy and current status for transitioning the IP-UBA pipeline to a new architecture.

Achievements

• Successfully refactored the data pipeline for Stage 01, enhancing maintainability and modularity.
• Implemented a robust orchestration layer for prediction processes.
• Developed a smoke testing framework to validate pipeline stages.

Pending Tasks

• Further testing and validation of the new pipeline architecture.
• Complete the migration of remaining modules to the new architecture.

Evidence

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• event_ids: []