DataBytes-Organisation/DiscountMate_new
Mar 2026 – May 2026
3 Months active
Languages Used
MarkdownPythonYAMLHCLSQLShellTerraform
Technical Skills
API IntegrationAirflowCI/CDData EngineeringData ProcessingDocker
anhvdq
PROFILE
Anhvdq
Over three months, contributed to the DataBytes-Organisation/DiscountMate_new repository by building and enhancing data ingestion and ETL pipelines that move and transform data from sources like MongoDB into PostgreSQL, leveraging Airflow for orchestration and MinIO for artifact storage. Developed Dockerized web scrapers with YAML-based brand configurations and improved logging for deployment consistency. Enhanced CI/CD workflows using GitHub Actions to enforce code quality and automate documentation. Refactored ETL processes for flexible date handling and retailer-specific organization, and integrated machine learning-ready features for pricing insights. Utilized Python, SQL, and Terraform to strengthen cloud infrastructure, data reliability, and collaborative engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
9
Lines of code
8,872,650
Activity Months3
Your Network
47 peopleWork History
May 2026
8 Commits • 5 Features
May 1, 20262026-05 Monthly Summary: This period delivered end-to-end data ingestion and ETL enhancements across the DiscountMate_new pipelines, drove ML-enabled pricing insights, and strengthened governance through updated docs and contribution guidelines. Major bugs fixed included stabilizing Cloud Run job resource allocations and cleaning up the Woolworths pipeline, improving reliability and data freshness. Key outcomes include multi-source ingestion via Cloud Run and Cloud Scheduler with GCS Bronze inputs and tuned resources; Aldi ETL refactor to improve data processing and PostgreSQL integration; Woolworths ETL enhancements with a new pricing workflow, SQL transformations, PostgreSQL synchronization, and bronze storage; addition of ML-ready prediction columns in dim_products to support pricing decisions; and comprehensive Data Engineering docs and GitHub guidelines to improve collaboration and governance. Technologies demonstrated include GCP Cloud Run and Cloud Scheduler, GCS, PostgreSQL, SQL data transformations, ML-ready data modeling, and governance/documentation practices.
8 Commits • 5 Features
May 1, 20262026-05 Monthly Summary: This period delivered end-to-end data ingestion and ETL enhancements across the DiscountMate_new pipelines, drove ML-enabled pricing insights, and strengthened governance through updated docs and contribution guidelines. Major bugs fixed included stabilizing Cloud Run job resource allocations and cleaning up the Woolworths pipeline, improving reliability and data freshness. Key outcomes include multi-source ingestion via Cloud Run and Cloud Scheduler with GCS Bronze inputs and tuned resources; Aldi ETL refactor to improve data processing and PostgreSQL integration; Woolworths ETL enhancements with a new pricing workflow, SQL transformations, PostgreSQL synchronization, and bronze storage; addition of ML-ready prediction columns in dim_products to support pricing decisions; and comprehensive Data Engineering docs and GitHub guidelines to improve collaboration and governance. Technologies demonstrated include GCP Cloud Run and Cloud Scheduler, GCS, PostgreSQL, SQL data transformations, ML-ready data modeling, and governance/documentation practices.
May 2026
April 2026
2 Commits • 2 Features
Apr 1, 2026April 2026: Focused on strengthening data quality, processing flexibility, and deployment reliability for DiscountMate_new. Delivered two key features addressing data ingestion and automation, plus improvements to repository hygiene and CI/CD workflows to enable faster, safer iterations.
April 2026
2 Commits • 2 Features
Apr 1, 2026April 2026: Focused on strengthening data quality, processing flexibility, and deployment reliability for DiscountMate_new. Delivered two key features addressing data ingestion and automation, plus improvements to repository hygiene and CI/CD workflows to enable faster, safer iterations.
March 2026
4 Commits • 2 Features
Mar 1, 2026March 2026 performance summary for DataBytes-Organisation/DiscountMate_new: Delivered end-to-end data ingestion pipeline from MongoDB to PostgreSQL using Airflow, with artifacts stored in MinIO, plus documentation and a CI workflow enforcing code quality. Dockerized scrapers with brand configurations (ALDI, Coles, IGA, Woolworths) and enhanced logging/error handling to ensure deployment consistency across environments. These efforts improved data reliability, reduced time-to-insight, and elevated code quality and maintainability.
4 Commits • 2 Features
Mar 1, 2026March 2026 performance summary for DataBytes-Organisation/DiscountMate_new: Delivered end-to-end data ingestion pipeline from MongoDB to PostgreSQL using Airflow, with artifacts stored in MinIO, plus documentation and a CI workflow enforcing code quality. Dockerized scrapers with brand configurations (ALDI, Coles, IGA, Woolworths) and enhanced logging/error handling to ensure deployment consistency across environments. These efforts improved data reliability, reduced time-to-insight, and elevated code quality and maintainability.
March 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.8%
Architecture90.0%
Performance85.8%
AI Usage31.4%
Skills & Technologies
Programming Languages
HCLMarkdownPythonSQLShellTerraformYAML
Technical Skills
API IntegrationAirflowCI/CDCloud ComputingCloud Infrastructure ManagementCloud IntegrationData EngineeringData ProcessingDevOpsDockerDuckDBETLETL DevelopmentETL developmentETL processes
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline