dataforgoodfr/13_brigade_coupes_rases
Mar 2025 – Mar 2025
1 Month active
Languages Used
BashDockerfilePythonShellYAML
Technical Skills
API IntegrationCloud Storage (S3)Cron JobsData EngineeringData PipelineData Processing
mandresyandri
PROFILE
Mandresyandri
Mandresy Andriantsoanavalona developed an automated deforestation data processing pipeline for the dataforgoodfr/13_brigade_coupes_rases repository, focusing on reliability and maintainability. The solution leveraged Python and Docker to create a scheduled ETL process that ingests data from Zenodo, transforms it into vector format, and performs geospatial analysis to identify clear-cut areas. By integrating cron-like scheduling and cloud storage with S3, Mandresy enabled repeatable, hands-off monitoring of deforestation events. The work included refactoring scripts for improved logging and parameter handling, as well as removing obsolete components, resulting in a streamlined, scalable pipeline that supports data-driven decision-making for partner organizations.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
1
Lines of code
1,078
Activity Months1
Your Network
17 peopleShared Repositories
17
Work History
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for dataforgoodfr/13_brigade_coupes_rases: Delivered an end-to-end automated deforestation data processing pipeline and implemented code quality improvements with a focus on reliability, maintainability, and scalability. The project now features a Dockerized scheduled ETL that pulls data from Zenodo, transforms to vector format, performs spatial analysis to identify clear-cut areas, and executes on a cron-like schedule. These updates enable timely, repeatable deforestation monitoring, reduce manual intervention, and support data-driven decision-making for partner organizations..
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for dataforgoodfr/13_brigade_coupes_rases: Delivered an end-to-end automated deforestation data processing pipeline and implemented code quality improvements with a focus on reliability, maintainability, and scalability. The project now features a Dockerized scheduled ETL that pulls data from Zenodo, transforms to vector format, performs spatial analysis to identify clear-cut areas, and executes on a cron-like schedule. These updates enable timely, repeatable deforestation monitoring, reduce manual intervention, and support data-driven decision-making for partner organizations..
March 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability84.0%
Architecture76.0%
Performance76.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashDockerfilePythonShellYAML
Technical Skills
API IntegrationCloud Storage (S3)Cron JobsData EngineeringData PipelineData ProcessingDevOpsDockerETLGeospatial AnalysisGeospatial Data ProcessingPython ScriptingScripting
Repositories Contributed To
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