### Course Overview
- **Course Title:** Certified Data Engineering & Pipelines
- **Instructor:** Muhammad Shafiq (Data Scientist | AI & ML Engineer | Lecturer | Researcher)
- **Target Audience:**
- Aspiring **data engineers**
- **Cloud professionals** transitioning to data roles
- **Software engineers** expanding into data infrastructure
- **Data analysts** aiming for advanced pipeline skills
- **Prerequisites:**
- Basic **Python** and **SQL** knowledge
- Familiarity with **cloud computing** (AWS/GCP) recommended
### Curriculum Highlights
- **Key Topics Covered:**
- **Pipeline orchestration** with **Apache Airflow** (DAGs, scheduling, monitoring)
- **Distributed data processing** using **Apache Spark (PySpark)**
- **Cloud-native ETL/ELT** solutions (AWS/GCP serverless tools)
- **Data lakes & warehouses** (S3, GCS, Snowflake, Redshift)
- **Infrastructure as Code (IaC)** for scalable data pipelines
- **Error handling, performance tuning, and monitoring**
- **Key Skills Learned:**
- Designing **production-ready data pipelines**
- Implementing **scalable ETL/ELT workflows**
- Integrating **cloud services** with **Apache Airflow**
- Optimizing **PySpark jobs** for large-scale data
- Deploying **data lakes** and **warehouses** with IaC
### Course Format
- **Duration:** 3 **practice tests** (hands-on assessments)
- **Format:** **Self-paced online course** with mobile access
- **Resources:**
- **Downloadable materials** (code templates, architecture diagrams)
- **Quizzes** and **practical exercises**
- **Portfolio-ready project** (end-to-end pipeline deployment)
