GCP Data Engineering Online Training

This is a complete hands-on GCP Data Engineering Training covering
all major Google Cloud data services — BigQuery, Dataflow, Dataproc,
Pub/Sub, Cloud Composer, Dataplex, Data Catalog, Datastream and more.

You will build real production pipelines on actual GCP accounts with
hands-on labs, real datasets and industry patterns used at Google,
Deloitte, TCS and Accenture.

What You Will Learn

✅ Build scalable data lakes on Cloud Storage — Bronze, Silver, Gold
✅ Master BigQuery — best practices, optimization, security
✅ Build batch pipelines using Dataproc and PySpark
✅ Build streaming pipelines using Dataflow and Apache Beam
✅ Handle real-time events with Pub/Sub
✅ Implement CDC pipelines using Datastream
✅ Orchestrate pipelines end-to-end with Cloud Composer (Airflow)
✅ Govern data with Dataplex and Data Catalog
✅ Deploy CI/CD using Cloud Build, GitHub Actions and Terraform
✅ Monitor pipelines with Cloud Monitoring and Cloud Logging
✅ Build 3 end-to-end capstone projects for your portfolio
✅ Clear the GCP Professional Data Engineer certification exam

Course Modules at a Glance

Module 1 — Cloud & GCP Fundamentals
Module 2 — GCP IAM & Security
Module 3 — Cloud Storage — GCP Data Lake
Module 4 — BigQuery Core Concepts & Architecture
Module 5 — BigQuery SQL & Advanced Querying
Module 6 — BigQuery Best Practices & Optimization
Module 7 — BigQuery Python & Automation
Module 8 — Cloud SQL — Managed Relational Database
Module 9 — Cloud Spanner — Globally Distributed Database
Module 10 — Cloud Bigtable — Wide Column NoSQL
Module 11 — Pub/Sub — Real-Time Messaging
Module 12 — Dataflow — Unified Batch & Streaming
Module 13 — Dataproc — Managed Spark & Hadoop
Module 14 — Cloud Composer — Pipeline Orchestration
Module 15 — Dataplex — Data Mesh & Governance
Module 16 — Data Catalog — Metadata Management
Module 17 — Datastream — Change Data Capture
Module 18 — GCP Pipeline Architecture Patterns
Module 19 — CI/CD for GCP Data Engineering
Module 20 — GCP Monitoring & Observability
Module 21 — End-to-End Capstone Projects
Module 22 — GCP Professional Data Engineer Exam Prep

Who Is This Course For

👨‍💻 Developers moving into GCP Data Engineering
🔄 ETL developers upgrading to cloud-native GCP tools
☁️ AWS or Azure engineers adding GCP to their skills
📊 Data Analysts transitioning into Data Engineering
🎓 Graduates targeting Data Engineer roles at top MNCs
🏆 Professionals preparing for GCP Professional Data Engineer exam

Prerequisites

– Basic Python — loops, functions, file handling
– Basic SQL — SELECT, JOIN, GROUP BY
– No prior GCP experience needed — start from scratch
– Laptop with internet — all labs run on GCP free tier

GCP Services Covered

☁️ Cloud Storage — Data Lake
🔍 BigQuery — Serverless Data Warehouse
⚡ Dataflow — Batch & Streaming (Apache Beam)
🖥️ Dataproc — Managed Spark & Hadoop
📨 Pub/Sub — Real-Time Messaging
🗄️ Cloud SQL — Managed PostgreSQL & MySQL
🌐 Cloud Spanner — Global Distributed Database
📦 Cloud Bigtable — Wide Column NoSQL
🎼 Cloud Composer — Apache Airflow
🏛️ Dataplex — Data Mesh & Governance
📋 Data Catalog — Metadata & Discovery
🔄 Datastream — Change Data Capture CDC
🔐 IAM & Secret Manager — Security
📊 Cloud Monitoring & Logging — Observability
🚀 Cloud Build & GitHub Actions — CI/CD
🏗️ Terraform — Infrastructure as Code

3 Capstone Projects

Project 1 — Batch Medallion Data Platform
Cloud SQL → Datastream → GCS → Dataproc →
BigQuery → Composer Orchestration → Looker Studio

Project 2 — Real-Time Streaming Pipeline
Pub/Sub → Dataflow Streaming → BigQuery →
Looker Studio Real-Time Dashboard

Project 3 — Serverless Event-Driven Pipeline
GCS Upload → Cloud Functions → Dataflow →
BigQuery → Dataplex DQ → CI/CD with Terraform