DevOps Engineer & Cloud Enthusiast (Azure) | Data Engineer (Python, SQL, Spark, Airflow)
Based in Morocco 🇲🇦, I'm passionate about transforming raw data into actionable insights through automated, scalable, and reliable data infrastructure.
I architect end-to-end data solutions that bridge the gap between data engineering and DevOps, ensuring data flows seamlessly from source to insight with enterprise-grade reliability.
Core Philosophy:
"Data without automation is just expensive storage.
Automation without monitoring is just expensive chaos."
Current Mission:
Building next-generation data platforms that scale effortlessly
and deliver real-time insights that drive business decisions.Programming & Scripting
Data Processing & Orchestration
Databases & Storage
Modeling & Architecture
Cloud & Big Data
Cloud Providers
Azure Data Services
Big Data
Visualization & Analytics
DevOps & Infrastructure
Enterprise-Grade Data Flow Automation Platform
What it does:
- Automates Apache NiFi data flow deployments across multiple environments
- Implements GitOps workflow with branch-based promotion strategy
- Integrates version control with NiFi Registry for complete audit trails
- Eliminates manual deployment errors through intelligent automation
Business Impact:
Deployment Time: 2 hours → 15 minutes (87.5% reduction)
Manual Errors: 15% failure rate → 0% (100% elimination)
Developer Onboarding: 2 days → 4 hours (75% reduction)
Technical Architecture:
Infrastructure: Terraform → Azure (Dev/Staging/Prod environments)
CI/CD Pipeline: GitHub Actions → Change Detection → Automated Deployment
Version Control: NiFi Registry ↔ Git Hooks → Automatic Synchronization
GitOps Flow: develop → staging → main (PR-based promotion)
Intelligent Data Scraping & Visualization Platform
Demo Credentials: bob / bobpass
What it does:
- Extracts and structures complex gaming data from web sources
- Implements robust data validation and quality checks
- Provides interactive analytics through modern dashboards
- Supports multiple export formats with optimized performance
Technical Highlights:
Architecture: Web Scraping → Data Processing → Storage → Visualization
Pipeline: Selenium + BeautifulSoup → Pandas → PostgreSQL → Streamlit
Performance: Real-time filtering • Advanced search • Export optimization
Enterprise IoT Data Streaming Platform
What it does:
- Ingests high-volume IoT sensor data in real-time
- Implements Change Data Capture for database synchronization
- Processes streaming data with fault-tolerant architecture
- Delivers actionable insights through interactive dashboards
Technical Architecture:
Data Flow: IoT Sensors → Kafka → Spark → PostgreSQL → Superset
CDC Pipeline: Database Changes → Debezium → Kafka → Stream Processing
Monitoring: Real-time metrics • Data quality validation • Alert systems
