omar-gamal99/talabat_bootcamp
May 2025 – May 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
AirflowBigQueryCloud Data TransferDAG orchestrationData EngineeringETL
abdullaheldamanhoury
PROFILE
Abdullaheldamanhoury
During May 2025, work on the omar-gamal99/talabat_bootcamp repository centered on building scalable data pipelines using Airflow and Google Cloud Platform. The developer delivered a reusable Airflow framework, starting with a Hello Airflow DAG to validate orchestration patterns. They implemented an end-to-end ETL pipeline that extracts orders data from PostgreSQL, exports it as JSON to Google Cloud Storage, and loads it into BigQuery with overwriting logic, including downstream triggers for cleaning and reporting. A dynamic, YAML-driven DAG factory was also introduced, enabling configurable and scalable pipeline generation. All features were developed in Python and YAML, with ongoing stability refinements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
3
Lines of code
568
Activity Months1
Your Network
22 peopleWork History
May 2025
12 Commits • 3 Features
May 1, 2025May 2025 monthly summary for omar-gamal99/talabat_bootcamp focused on delivering scalable data pipelines and establishing a reusable Airflow framework. Key features delivered include a starter Hello Airflow DAG, a complete Orders data ETL pipeline that moves data from PostgreSQL to Google Cloud Storage (as JSON) and into BigQuery with overwriting, plus downstream orchestration for cleaning and reporting. A Dynamic YAML-driven DAG factory was introduced to enable configurable, scalable pipeline generation. No major bugs were reported this month; however, several small refinements and stability improvements were executed across the ETL components.
12 Commits • 3 Features
May 1, 2025May 2025 monthly summary for omar-gamal99/talabat_bootcamp focused on delivering scalable data pipelines and establishing a reusable Airflow framework. Key features delivered include a starter Hello Airflow DAG, a complete Orders data ETL pipeline that moves data from PostgreSQL to Google Cloud Storage (as JSON) and into BigQuery with overwriting, plus downstream orchestration for cleaning and reporting. A Dynamic YAML-driven DAG factory was introduced to enable configurable, scalable pipeline generation. No major bugs were reported this month; however, several small refinements and stability improvements were executed across the ETL components.
May 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance83.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
AirflowBigQueryCloud Data TransferDAG orchestrationData EngineeringETLGCSGoogle Cloud PlatformGoogle Cloud StoragePostgreSQLPythonYAML
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