Implemented and tested data pipeline automation

• Day: 2025-05-28
• Time: 07:10 to 07:40
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Pipeline, Github Actions, CI/CD, Python, Sqlite

Description

Session Goal

The session aimed to implement and test an automated data pipeline for maintaining an updated local copy of a time series dataset using GitHub Actions.

Key Activities

• Pipeline Implementation: Detailed the setup of an automated pipeline using GitHub Actions to keep a local dataset updated.
• Local Testing: Conducted local tests of the data processing pipeline to ensure deterministic behavior before deploying to CI/CD.
• Bash Commands: Provided Bash commands to list disk usage, aiding in managing local storage during pipeline execution.
• Script Modification: Proposed and partially implemented modifications to a Python download script to exclude certain file types and sizes.
• Onboarding Guide: Created an onboarding guide for a public data ingestion pipeline, focusing on datasets from the Argentine Ministry of Economy.
• CSV Export Script: Reviewed the export_csv.py script for extracting data from SQLite to CSV.
• Error Diagnosis: Identified and suggested solutions for a desynchronization error in the 05_export_csv.py script.

Achievements

• Successfully set up a GitHub Actions pipeline for dataset management.
• Completed local testing of the CI/CD pipeline.
• Developed onboarding documentation for data ingestion pipelines.

Pending Tasks

• Finalize the modification of the Python download script to make file size limits configurable.
• Resolve the desynchronization error in the 05_export_csv.py script by incorporating dynamic data extraction logic.

Evidence

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