DataBytes-Organisation/DiscountMate_new
May 2026 – May 2026
1 Month active
Languages Used
PythonYAML
Technical Skills
AirflowData EngineeringETL DevelopmentPostgreSQLPythonSpark
Margie Licup
PROFILE
Margie Licup
Developed and delivered an end-to-end ETL pipeline for the DataBytes-Organisation/DiscountMate_new repository, enabling IGA data ingestion from MongoDB to MinIO, transformation and processing with Spark, and loading into PostgreSQL for analytics enablement. The solution incorporated Airflow for orchestration, implemented robust data quality checks, and included mapping logic for products, categories, and retailers to ensure analytics-ready data. By focusing on data validation and a silver upsert strategy, the pipeline improved data reliability and accessibility for reporting. Work was completed using Python and YAML, with an emphasis on scalable ETL development and enhanced business insight through improved data engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,436,006
Activity Months1
Your Network
146 peopleSame Organization
@deakin.edu.au99
JustinMember
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CodeNinja4GitMember
Adrian FariaMember
RynGMMember
Brett McBrideMember
bshean1Member
Cjay BasaMember
Stefan CucosMember
Work History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 monthly summary for DataBytes-Organisation/DiscountMate_new: Delivered an end-to-end IGA Data ETL iNGEST and Analytics Enablement, establishing a robust data pipeline from MongoDB to MinIO, Spark-based processing, and loading into PostgreSQL. Implemented transformations for product, category, and retailer mapping, with data quality checks and a silver upsert to unlock analytics-ready data for reporting. No major bugs reported this month; pipeline improvements improved data reliability, accessibility for analytics, and overall business insight.
1 Commits • 1 Features
May 1, 2026May 2026 monthly summary for DataBytes-Organisation/DiscountMate_new: Delivered an end-to-end IGA Data ETL iNGEST and Analytics Enablement, establishing a robust data pipeline from MongoDB to MinIO, Spark-based processing, and loading into PostgreSQL. Implemented transformations for product, category, and retailer mapping, with data quality checks and a silver upsert to unlock analytics-ready data for reporting. No major bugs reported this month; pipeline improvements improved data reliability, accessibility for analytics, and overall business insight.
May 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
AirflowData EngineeringETL DevelopmentPostgreSQLPythonSpark
Repositories Contributed To
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