DataBytes-Organisation/DiscountMate_new
Apr 2025 – May 2025
2 Months active
Languages Used
MarkdownPythonSQL
Technical Skills
Data CleaningData EngineeringData ExportingData LoadingMongoDBPandas
Van An Duong
PROFILE
Van An Duong
Developed and enhanced data engineering pipelines for the DiscountMate_new repository, focusing on synthetic data generation, cleaning, and automated workflow orchestration. Built a comprehensive suite to generate and structure synthetic datasets for users, stores, products, and transactions, loading them into MongoDB and preparing them for SQL-based analytics. Improved data integrity by implementing idempotent ingestion with upsert logic and streamlined data export utilities for cross-environment portability. Automated data cleaning to remove redundant columns and integrated dbt execution within Airflow DAGs, reducing manual preparation and accelerating analytics delivery. Leveraged Python, SQL, and Airflow to ensure reliable, maintainable, and scalable data workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
827
Activity Months2
Your Network
47 peopleWork History
May 2025
3 Commits • 2 Features
May 1, 2025May 2025: Delivered end-to-end data pipeline enhancements in DiscountMate_new, focusing on data quality, reliability, and business value. Implemented synthetic data cleaning to automatically drop redundant_ columns across six core tables, with a validation test column added in create_dirty_data.py. Implemented an Airflow DAG to execute dbt end-to-end (install dependencies, compile, seed data, run the data model layers: staging, snapshots, intermediate, marts) and cleaned up the DAG by removing an unused BashOperator task. These changes reduce manual data prep, accelerate data availability in the data warehouse, and improve pipeline stability for analytics and reporting.
3 Commits • 2 Features
May 1, 2025May 2025: Delivered end-to-end data pipeline enhancements in DiscountMate_new, focusing on data quality, reliability, and business value. Implemented synthetic data cleaning to automatically drop redundant_ columns across six core tables, with a validation test column added in create_dirty_data.py. Implemented an Airflow DAG to execute dbt end-to-end (install dependencies, compile, seed data, run the data model layers: staging, snapshots, intermediate, marts) and cleaned up the DAG by removing an unused BashOperator task. These changes reduce manual data prep, accelerate data availability in the data warehouse, and improve pipeline stability for analytics and reporting.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025Concise April 2025 monthly summary for DataBytes DiscountMate_new focusing on business value and technical excellence. Delivered a comprehensive synthetic data provisioning pipeline and related tooling to accelerate testing, analytics, and data integrity.
April 2025
2 Commits • 1 Features
Apr 1, 2025Concise April 2025 monthly summary for DataBytes DiscountMate_new focusing on business value and technical excellence. Delivered a comprehensive synthetic data provisioning pipeline and related tooling to accelerate testing, analytics, and data integrity.
Activity
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Quality Metrics
Correctness86.0%
Maintainability84.0%
Architecture84.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonSQL
Technical Skills
AirflowBashData CleaningData EngineeringData ExportingData LoadingDatabase ManagementMongoDBPandasPythonSQLSQLAlchemySynthetic Data Generationdbt
Repositories Contributed To
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