Projects#
Here are some of my featured projects and case studies in data engineering and software development.
Featured Projects#
A comprehensive case study of implementing a cloud-based analytics platform using Azure services, including real-time
data processing, data lake architecture, and analytics workspace.
An overview of automated data pipeline development and implementation, focusing on efficiency and scalability.
Project Categories#
Data Engineering#
- ETL/ELT Development
- Data Pipeline Automation
- Data Warehouse Design
- Real-time Processing
Cloud Computing#
- Azure Services Implementation
- Cloud Migration
- Container Orchestration
- Serverless Architecture
Analytics#
- Business Intelligence
- Data Visualization
- Performance Optimization
- Data Quality Assurance
Technologies Used#
- Cloud Platforms: Azure, AWS
- Data Processing: Apache Spark, Hadoop, Hive
- Containerization: Docker, Kubernetes
- Programming: Python, SQL
- ETL Tools: Azure Data Factory, Databricks
- Monitoring: Azure Monitor, Prometheus
- Version Control: Git, GitHub
- CI/CD: Jenkins, GitHub Actions
Get in Touch#
Interested in discussing a project or collaboration? Feel free to reach out:
Competitor Ad Targeting Analysis with User Personas Project Overview Led a comprehensive analysis of competitor advertising strategies across popular Austrian websites. The project involved creating simulated user personas to identify which demographic groups were being targeted by different brands, and with what intensity.
Business Context Understanding how competitors target their advertising is crucial for optimizing marketing strategies. This project provided actionable insights into how different brands were allocating their advertising budgets across demographic segments in the Austrian market.
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Python Watchdog YAML-Based ETL Pipeline for Azure Data Lake Project Overview Developed a robust, event-driven ETL pipeline that monitors filesystem events and automatically processes and uploads data to Azure Data Lake Storage Gen2. The system used YAML configuration files for pipeline definition, making it highly configurable and maintainable.
Business Context The business needed a flexible solution to continuously monitor specific directories for new data files, process them according to predefined rules, and reliably upload the results to cloud storage. This enabled near real-time data processing without the complexity of a full streaming solution.
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Led the development of a cloud-based analytics platform at A1 Telekom Austria, enhancing data processing and decision-making capabilities.
Data Pipeline Automation Overview This project implements a robust data pipeline automation system that processes and transforms data from multiple sources into a unified data warehouse. The system is designed to be scalable, maintainable, and easily extensible.
Key Features Automated data extraction from various sources (APIs, databases, files) Data validation and quality checks Incremental data loading Error handling and retry mechanisms Comprehensive logging and monitoring Automated testing suite Architecture The system is built using a modern data stack:
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