End-to-End GCP Data Pipeline Project | Airflow + Dataproc +BigQuery|Data Engineering

Welcome to the Data Engineering Course 🚀 In this session, we build a complete End-to-End GCP Data Engineering Pipeline using real-world cloud services and workflow orchestration concepts. This is one of the most important practical sessions for Data Engineering interviews and real project understanding. 🔹 Technologies Covered: ✅ Google Cloud Storage (GCS) ✅ Dataproc (PySpark) ✅ BigQuery ✅ Cloud Composer (Airflow) ✅ PySpark Transformations ✅ XComs in Airflow 🔹 End-to-End Pipeline Flow: 1️⃣ Upload sample CSV data into GCS 2️⃣ Airflow DAG validates file existence in GCS 3️⃣ Airflow submits PySpark job to Dataproc 4️⃣ PySpark transforms the data 5️⃣ Processed data written back to GCS 6️⃣ Airflow loads transformed data into BigQuery 7️⃣ BigQuery validation query execution 8️⃣ XComs used for metadata transfer between tasks 🔹 What You Will Learn: 📌 Real-world GCP architecture 📌 Airflow orchestration concepts 📌 Dataproc job execution 📌 BigQuery integration 📌 Production-level pipeline understanding 📌 Interview-focused project explanation 🎯 This session is highly important for: Data Engineering Interviews GCP Interview Preparation Real-time Pipeline Understanding Cloud Data Engineering Projects 🚀 Upcoming Sessions: Real-time Streaming Pipelines Advanced Airflow Concepts BigQuery Optimization End-to-End Data Engineering Projects 👉 Subscribe for complete Data Engineering roadmap and interview preparation. #GCP #DataEngineering #BigQuery #Dataproc #Airflow #CloudComposer #PySpark #GoogleCloud