aws-mwaa/upstream-to-airflow
Apr 2026 – Apr 2026
1 Month active
Languages Used
Python
Technical Skills
backend developmentdatabase managementunit testing
Leonardo Soares
PROFILE
Leonardo Soares
Worked on the aws-mwaa/upstream-to-airflow repository to enhance reliability in asset-triggered DAG scheduling. Addressed a critical edge case by implementing logic in Python to skip asset-triggered DAGs lacking a SerializedDagModel, ensuring that premature DAG runs are avoided and pending asset events are preserved for future evaluation. The solution incorporated backend development skills, database management, and unit testing with pytest, aligning test data ordering with production database constraints. Improved observability through enhanced debug logging and maintained release hygiene. This work reduced invalid DAG runs, conserved resources, and contributed to more predictable scheduling behavior in complex Airflow environments.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
144
Activity Months1
Your Network
147 peopleSame Organization
@outlook.com.br13
Work History
April 2026
1 Commits
Apr 1, 2026April 2026 performance summary for aws-mwaa/upstream-to-airflow: Delivered a critical reliability improvement to asset-triggered DAG handling by adding guards for missing SerializedDagModel. The change ensures asset-triggered DAGs without a corresponding SerializedDagModel are skipped and their AssetDagRunQueue entries are retained for evaluation once serialization becomes available. This reduces premature DAG runs and stabilizes scheduling in edge cases. The work included unit tests validating the behavior, improved logging for debugging, and alignment with production DB constraints through deliberate insertion-order changes and test adjustments. Business value: fewer invalid runs, reduced wasted resources, and more predictable DAG schedules. Technologies used: Python, scheduler logic, unit testing (pytest), logging, and database FK ordering considerations.
1 Commits
Apr 1, 2026April 2026 performance summary for aws-mwaa/upstream-to-airflow: Delivered a critical reliability improvement to asset-triggered DAG handling by adding guards for missing SerializedDagModel. The change ensures asset-triggered DAGs without a corresponding SerializedDagModel are skipped and their AssetDagRunQueue entries are retained for evaluation once serialization becomes available. This reduces premature DAG runs and stabilizes scheduling in edge cases. The work included unit tests validating the behavior, improved logging for debugging, and alignment with production DB constraints through deliberate insertion-order changes and test adjustments. Business value: fewer invalid runs, reduced wasted resources, and more predictable DAG schedules. Technologies used: Python, scheduler logic, unit testing (pytest), logging, and database FK ordering considerations.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
backend developmentdatabase managementunit testing
Repositories Contributed To
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