🚀Job-Ready GCP & Airflow Data Engineering Pipeline

✅ What You’ll Build & Learn

1. GCP Data Engineering Pipeline (Dataproc Use Case)

• Dataproc Cluster Setup & PySpark Job Execution
• Apache Spark for Distributed Data Processing
• Google Cloud Storage Integration (input/output)
• Step-by-step walkthroughs with live screenshots
• Role-specific interview questions aligned with GCP use cases

2. Airflow Pipeline Project (with Docker)

• Deploy Airflow locally using Docker
• Build, schedule, and monitor DAGs end-to-end
• Learn orchestration, dependency management, and failure handling
• Resume-ready project, cloud-ready design, and fast to set up

🎯 Why This Product Stands Out

• Portfolio-Ready: Showcase two real-world projects on your resume.
• Interview-Focused: Practice with questions that tie directly to the pipelines you’ll build.
• Hands-On Learning: Follow annotated screenshots, explanations, and working code.
• Beginner-Friendly, Yet Advanced: Start with Docker-based Airflow, then scale up with GCP Data Engineering.

💡 Even if you don’t finish the full pipelines, you’ll still gain project insights, architectural knowledge, and interview talking points — enough to confidently crack interviews and stand out as a job-ready Data Engineer.