Google Cloud Platform (GCP) Data Engineer & Architect Program

Google Cloud Platform (GCP) has emerged as a leading platform for data-driven innovation, enabling enterprises to harness the power of AI, analytics, and machine learning at scale. This 7-day corporate training program provides a comprehensive, hands-on understanding of GCP’s data engineering and cloud architecture capabilities. Participants will learn to design, build, and manage data processing systems, architect scalable cloud infrastructure, and optimize workloads for performance and cost. The course blends theory with real-world labs, preparing professionals to lead enterprise-grade data and cloud transformation projects using GCP tools such as BigQuery, Dataflow, Pub/Sub, and Vertex AI.

Objectives of the Training

Understand GCP architecture, core services, and networking fundamentals.
Learn to design and manage scalable data pipelines and analytics systems.
Master GCP services such as BigQuery, Dataflow, Pub/Sub, and Cloud Storage.
Implement best practices for security, cost optimization, and automation.
Architect end-to-end data solutions using GCP for real-time analytics and AI applications.

Prerequisites

Familiarity with data processing, SQL, and ETL concepts.
Basic understanding of cloud computing principles and networking.
Prior experience with Python, Java, or data visualization tools is beneficial.
No prior GCP experience required — the course covers both foundational and advanced topics.

What You Will Learn

GCP core architecture, IAM, and networking fundamentals.
Data ingestion, transformation, and analysis using GCP data services.
Cloud storage management, security, and lifecycle policies.
Designing scalable architectures using Compute Engine, GKE, and App Engine.
Real-time data streaming, orchestration, and machine learning integration.
Cloud cost management, monitoring, and performance tuning.

Target Audience

This training is ideal for Data Engineers, Cloud Architects, Data Scientists, and IT Managers involved in designing, developing, or managing cloud-based data solutions. It is also suitable for professionals seeking to transition into cloud data engineering roles or pursue GCP certifications.

Detailed 7-Day Curriculum

Day 1 – Introduction to GCP and Cloud Architecture Fundamentals (6 Hours)

Session 1: GCP Global Infrastructure – Regions, Zones, and Network Design.
Session 2: GCP Services Overview – Compute, Storage, Networking, and IAM.
Session 3: GCP Console, SDK, and Cloud Shell Setup.
Hands-on: Creating and Managing Resources Using GCP Console and CLI.

Day 2 – Compute and Storage on GCP (6 Hours)

Session 1: Compute Options – Compute Engine, App Engine, and Kubernetes Engine (GKE).
Session 2: Cloud Storage Solutions – Buckets, Object Lifecycle, and Data Encryption.
Session 3: Managing Persistent Disks and File Systems.
Workshop: Deploying a Scalable Web Application using Compute Engine.

Day 3 – Data Engineering on GCP: Ingestion and Processing (6 Hours)

Session 1: Building Data Pipelines with Dataflow and Apache Beam.
Session 2: Real-Time Data Streaming using Pub/Sub and Dataflow.
Session 3: Batch vs. Streaming Data Architectures – Use Case Analysis.
Hands-on: Creating and Managing a Streaming Pipeline using Pub/Sub and Dataflow.

Day 4 – Advanced Analytics and Big Data Solutions (6 Hours)

Session 1: Introduction to BigQuery – Data Warehouse Design and Optimization.
Session 2: Querying and Managing Large Datasets using SQL and BI Tools.
Session 3: Integrating BigQuery with Data Studio for Business Analytics.
Workshop: Building a Real-Time Analytics Dashboard on BigQuery.

Day 5 – Machine Learning and AI Integration with Vertex AI (6 Hours)

Session 1: Introduction to Vertex AI and AutoML Capabilities.
Session 2: Building, Training, and Deploying ML Models using GCP Services.
Session 3: Integrating ML Workflows with Data Pipelines.
Hands-on: Creating and Deploying a Predictive Model using Vertex AI.

Day 6 – Security, Monitoring, and Cost Optimization (6 Hours)

Session 1: Identity and Access Management (IAM) and Role-Based Access Control.
Session 2: Security, Encryption, and Compliance Best Practices in GCP.
Session 3: Monitoring and Logging using Cloud Monitoring and Operations Suite.
Workshop: Setting Up Monitoring Dashboards and Budget Alerts for GCP Projects.

Day 7 – Capstone Project & Future of Cloud Data Engineering (6 Hours)

Session 1: Capstone Project – Designing an End-to-End Data Analytics Solution on GCP.
Session 2: Project Presentation and Review of Architecture Design.
Session 3: Future Trends – Multi-Cloud Data Architectures, AI-Driven Automation, and Sustainability in Cloud Data Operations.
Panel Discussion: Building a Data-Driven Enterprise on Google Cloud.

Capstone Project

Participants will design and implement a complete data engineering and analytics solution using Google Cloud Platform. The project will include data ingestion, transformation, storage, and analysis using BigQuery, Dataflow, and Pub/Sub. Teams will demonstrate their solutions with end-to-end architecture diagrams and performance insights.

Future Trends in GCP Data Engineering and Cloud Architecture

The future of cloud data engineering lies in the convergence of AI, automation, and multi-cloud strategies. GCP continues to lead innovation with tools such as Vertex AI, Looker, and Dataform for unified analytics and machine learning workflows. As data volumes and business complexity grow, enterprises that master GCP data architecture will gain unparalleled agility, scalability, and competitive advantage in decision intelligence and cloud-driven transformation.