omar-gamal99/talabat_bootcamp
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
AirflowData EngineeringETLGoogle Cloud PlatformPostgreSQLPython
Adham Hassan
PROFILE
Adham Hassan
Adham Hassan Ali developed two Airflow DAGs for the omar-gamal99/talabat_bootcamp repository, focusing on onboarding validation and automating backend data transfers. He implemented a Hello World DAG to verify Airflow setup, scheduled for daily execution with catchup disabled, ensuring a reliable onboarding process. Adham also engineered an ETL pipeline that exports data from PostgreSQL to CSV in Google Cloud Storage, loads it into BigQuery with schema auto-detection, and performs automated cleanup of temporary files. Using Python, Airflow, and Google Cloud Platform, his work established a repeatable, analytics-ready infrastructure, reducing manual intervention and supporting faster, more reliable data operations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
95
Activity Months1
Your Network
22 people
Work History
May 2025
2 Commits • 2 Features
May 1, 2025May 2025 — Summary for omar-gamal99/talabat_bootcamp: Delivered two Airflow DAGs that establish onboarding validation and automate backend data transfer to BigQuery, delivering measurable business value and solid infra foundation. Hello World DAG verifies Airflow setup with a daily run and catchup disabled. PostgreSQL to Google BigQuery ETL DAG exports a table to CSV in GCS, loads into BigQuery with schema auto-detection, and cleans up temporary files. These deliverables enable faster analytics, repeatable deployments, and reduced manual steps. No user-facing bug fixes were completed this month.
2 Commits • 2 Features
May 1, 2025May 2025 — Summary for omar-gamal99/talabat_bootcamp: Delivered two Airflow DAGs that establish onboarding validation and automate backend data transfer to BigQuery, delivering measurable business value and solid infra foundation. Hello World DAG verifies Airflow setup with a daily run and catchup disabled. PostgreSQL to Google BigQuery ETL DAG exports a table to CSV in GCS, loads into BigQuery with schema auto-detection, and cleans up temporary files. These deliverables enable faster analytics, repeatable deployments, and reduced manual steps. No user-facing bug fixes were completed this month.
May 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AirflowData EngineeringETLGoogle Cloud PlatformPostgreSQLPython
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