Google Professional Data Engineer (GCP) Course in Chennai

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Master cloud data engineering, big data processing, ETL pipelines, cloud data warehousing, real-time analytics, machine learning integration, and scalable data solutions with Edubrights' Google Professional Data Engineer (GCP) Course in Chennai

This advanced training is designed for students, freshers, software developers, data analysts, database administrators, data engineers, cloud engineers, DevOps professionals, AI practitioners, and working professionals who want to design, build, secure, and manage enterprise-grade data processing systems using Google Cloud Platform (GCP).

Gain hands-on experience with Google Cloud Storage, BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Vertex AI, ETL development, cloud data migration, workflow automation, and real-world cloud data engineering projects through practical industry use cases.

Key Highlights:

✅ Real-Time Cloud Data Engineering Projects & Enterprise Data Processing Use Cases

✅ Live Instructor-Led Training by Experienced Google Cloud Data Engineering Professionals

✅ Hands-On Practice with Google Cloud Platform (GCP) Services

✅ Google Cloud Fundamentals, Cloud Architecture & Data Engineering Workflow

✅ Google Cloud Storage, Cloud SQL, Cloud Spanner, Bigtable & Firestore Implementation

✅ BigQuery Data Warehousing, SQL Analytics & Query Performance Optimization

✅ Dataflow Pipelines, Apache Beam & Batch and Streaming Data Processing

✅ Cloud Pub/Sub, Event-Driven Architecture & Real-Time Data Ingestion

✅ Dataproc, Apache Spark, Hadoop & Large-Scale Data Processing

✅ Cloud Composer (Apache Airflow), ETL Automation & Workflow Orchestration

✅ Vertex AI Integration, Machine Learning Pipelines & Predictive Analytics

✅ Cloud Security, IAM, Data Governance, Compliance & Best Practices

✅ Monitoring, Logging, Performance Optimization & Cloud Cost Management

✅ Resume Building, Cloud Data Engineering Portfolio Development & Mock Interview Preparation

✅ Career Guidance, Placement Assistance & Google Professional Data Engineer Certification Support

✅ Flexible Online, Classroom & Weekend Training Options

✅ Corporate Training for IT Companies, Cloud Service Providers, Data Engineering Teams, Analytics Organizations, Software Development Companies & Enterprise Businesses

Build practical cloud data engineering expertise, design scalable data platforms, develop automated ETL pipelines, process large-scale batch and streaming data, implement cloud-native analytics solutions, integrate machine learning with Google Cloud services, create an impressive cloud portfolio, and become industry-ready for careers in Data Engineering, Cloud Data Engineering, Big Data Analytics, Data Platform Engineering, Cloud Consulting, and Enterprise Data Solutions.

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₹25000

₹45000

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Case Studies and Projects

20+

Hours of Training

20+

Placement Assurance

100%

Expert Support

yes

Support & Access

Lifetime

Certification

Yes

Skill Level

All

Language

All

Course Objectives

Q1. Why should I learn Google Professional Data Engineer (GCP)?

This course helps you develop practical skills in designing, building, securing, and managing scalable data solutions using Google Cloud Platform. You will learn industry-standard cloud data engineering practices while preparing for the Google Professional Data Engineer certification.

Q2. What practical knowledge will I gain from this course?

You will gain hands-on experience with BigQuery, Dataflow, Pub/Sub, Cloud Storage, Dataproc, Cloud Composer, and Vertex AI. Practical projects help you understand modern data pipelines, analytics, automation, and cloud-based data processing.

Q3. How does this course support career growth?

Organizations increasingly rely on cloud-based data platforms for analytics and business intelligence. This course helps strengthen your cloud data engineering skills and prepares you to work on enterprise-scale data projects using Google Cloud technologies.

Q4. What are the major learning outcomes of this training?

After completing this course, learners will be able to design secure data architectures, build scalable ETL pipelines, process batch and streaming data, integrate machine learning workflows, optimize cloud resources, and confidently prepare for the Google Professional Data Engineer certification exam.

Q5. What skills are required to become a Google Professional Data Engineer?

Develop practical skills in cloud data engineering, ETL development, SQL analytics, data pipeline design, cloud architecture, security, and machine learning integration.

Popular Techniques Covered in This Course

Google Cloud Platform

Data Engineering

BigQuery Analytics

Dataflow Pipelines

Cloud Pub/Sub

ETL Development

Cloud Databases

Data Security

Vertex AI Integration

Cloud Cost Optimization

Get Hands-on Knowledge about Real-Time Projects

Project 1

Enterprise Data Warehouse with BigQuery

Design and build a cloud-based enterprise data warehouse using Google BigQuery by integrating data from multiple business sources. This project helps learners gain practical experience in data modeling, SQL analytics, query optimization, reporting, and large-scale cloud data warehousing.

Project 2

Real-Time Data Streaming Pipeline

Develop a real-time data processing solution using Google Cloud Pub/Sub and Dataflow to collect, process, and analyze streaming data. Learners understand event-driven architecture, stream processing, data transformation, and real-time analytics using Google Cloud services.

Project 3

Automated ETL Workflow with Cloud Composer

Create an automated ETL pipeline using Cloud Composer (Apache Airflow) to extract, transform, validate, and load business data into BigQuery. This project focuses on workflow automation, task scheduling, dependency management, and cloud data pipeline orchestration.

Project 4

Predictive Analytics with Vertex AI

Build an end-to-end machine learning solution using Vertex AI to prepare data, train predictive models, deploy machine learning endpoints, and generate business insights. Learners gain hands-on experience in integrating machine learning with cloud data engineering workflows and deploying AI-powered analytics solutions.

Project 5

Legacy ERP Data Warehouse Migration and Performance Triage

Transition a standard serial database query into a multi-way parallel execution flow using Partition by Round-Robin components. Run execution profiling monitors to analyze data skewing patterns across active disks and securely de-partition the transformed records for downstream consumption.

Key Features

Edubrights offers Google Professional Data Engineer (GCP) Training in virtual mode with expert trainers. Here are the key features,

40 Hours Course Duration

100% Job Oriented Training

Industry Expert Faculties

Free Demo Class Available

Completed 500+ batches

Certification Guidance

Curriculum

Module 1: Google Cloud Platform Fundamentals

Introduction to Cloud Computing
Google Cloud Platform Overview
GCP Global Infrastructure
Identity and Access Management (IAM)
Resource Management
GCP Console and Cloud SDK

Module 2: Data Engineering Fundamentals

Data Engineering Concepts
Structured and Unstructured Data
Data Processing Lifecycle
Batch vs Streaming Data
Data Quality
Data Governance Basics

Module 3: Cloud Storage and Databases

Google Cloud Storage
Cloud SQL
Cloud Spanner
Firestore
Bigtable
Database Selection Strategies

Module 4: BigQuery Data Warehousing

BigQuery Architecture
Dataset Management
SQL Analytics
Query Optimization
Partitioning and Clustering
Data Visualization Integration

Module 5: Data Processing with Dataflow

Apache Beam Fundamentals
Dataflow Pipelines
Batch Processing
Stream Processing
Pipeline Monitoring
Performance Optimization

Module 6: Messaging and Data Ingestion

Cloud Pub/Sub
Event-Driven Architecture
Data Ingestion Pipelines
Real-Time Data Streaming
Messaging Best Practices
Integration Techniques

Module 7: Data Lakes and Big Data Processing

Dataproc
Hadoop on GCP
Spark Fundamentals
Data Lake Architecture
Big Data Analytics
Performance Tuning

Module 8: Workflow Automation

Cloud Composer
Apache Airflow
Workflow Scheduling
ETL Automation
Dependency Management
Monitoring Workflows

Module 9: Security and Compliance

IAM Best Practices
Data Encryption
Cloud Security
Network Security
Compliance Standards
Audit Logging

Module 10: Machine Learning Integration

Vertex AI Fundamentals
ML Data Preparation
Feature Engineering
Model Deployment
Prediction Pipelines
AI Integration

Module 11: Monitoring and Cost Optimization

Cloud Monitoring
Cloud Logging
Performance Monitoring
Cost Management
Resource Optimization
Operational Best Practices

Module 12: Google Professional Data Engineer Certification Preparation

Certification Exam Objectives
Case Study Discussions
Architecture Design Scenarios
Practice Assessments
Best Practices

- Certification Readiness

Receive Training From Our Skilled and Effective Trainers

Experience in the Industry Gain expertise from Ab Initio ETL developers who have built high-volume data processing pipelines for financial services, telecommunications, and retail organisations where Ab Initio powers mission-critical data integration.

Backgrounds at the Top Our Ab Initio trainers have worked at global banks, insurance companies, and telecom providers where Ab Initio's parallel processing capabilities handle billions of records in nightly batch ETL workloads.

Clear & Effective Teaching Ab Initio GDE, core components, DML, parallel processing, Conduct It workflow, EME metadata management, data profiling, and ETL best practices are explained with real enterprise data integration examples.

Hands-On Learning Focus Students build Ab Initio graphs, apply parallel partitioning, write DML and expressions, create Conduct It plans, manage versions in EME, and profile source data through structured hands-on ETL development labs.

Up-to-Date Knowledge Trainers keep content current with the latest Ab Initio platform releases, Ab Initio on cloud deployments, and evolving enterprise ETL and data integration best practices.

Earn an Industry-Recognized Google Professional Data Engineer Certification

Successfully completing the Google Professional Data Engineer (GCP) Course in Chennai demonstrates your ability to design, build, secure, monitor, and optimize enterprise data platforms using Google Cloud.

Course FAQs

1. Who can enroll in the Google Professional Data Engineer (GCP) Course?

This course is suitable for students, fresh graduates, software developers, data analysts, database administrators, cloud engineers, DevOps professionals, and working professionals who want to develop practical cloud data engineering skills using Google Cloud Platform.

2. Are there any prerequisites for learning Google Professional Data Engineer?

Basic knowledge of databases, SQL, cloud computing, and programming concepts is helpful but not mandatory. Familiarity with data processing concepts will make it easier to understand advanced cloud data engineering topics.

3. How long does it take to complete the Google Professional Data Engineer training?

The course duration depends on the training schedule and learning format. Most learners complete the training over several weeks through instructor-led classes, practical labs, hands-on projects, and certification preparation sessions.

4. Does the course include practical hands-on training?

Yes. The training includes hands-on labs using BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Vertex AI, and other Google Cloud services. Learners work on real-world data engineering scenarios to build practical experience.

5. Is this course aligned with the Google Professional Data Engineer certification exam?

Yes. The curriculum is designed according to the official Google Professional Data Engineer certification objectives and includes architecture design, data processing, security, machine learning integration, and certification-focused practice sessions.

6. What projects are included in the training?

Learners work on cloud data warehouse implementation, ETL pipeline development, real-time streaming analytics, workflow automation, machine learning integration, and enterprise data processing projects using Google Cloud services.

7. What job roles can I pursue after completing this course?

After completing the training, learners can explore opportunities such as Data Engineer, Cloud Data Engineer, Big Data Engineer, GCP Engineer, Cloud Consultant, Data Platform Engineer, Analytics Engineer, and Data Integration Specialist.

8. Is the Google Professional Data Engineer certification valuable?

Yes. The certification validates your ability to design, build, secure, and manage scalable data processing systems on Google Cloud Platform. It demonstrates practical cloud data engineering expertise recognized across industries.

9. Can beginners learn Google Professional Data Engineer?

Yes. Learners with basic knowledge of databases and cloud computing can begin this course. The curriculum gradually progresses from cloud fundamentals to advanced data engineering concepts using structured learning and practical exercises.

10. Why choose Google Professional Data Engineer Training in Chennai?

Chennai has a rapidly growing demand for cloud computing, analytics, and data engineering professionals. This training helps learners build industry-relevant Google Cloud skills aligned with current enterprise technology requirements.

11. What is the purpose of a Lookup File component inside an ETL graph?

A Lookup File loads an index of smaller datasets entirely into system memory. This allows processing components like Reformat to execute extremely fast, near-instantaneous key comparisons on active data streams without executing slower database queries.

12. Why are corporate consulting firms in India constantly recruiting Ab Initio developers?

Because major enterprise systems like international banks and global telecomm groups run their entire backend accounting systems on Ab Initio. The complete absence of open-source clones means qualified developers face zero competition from casual hobbyists.

13. Can experienced data professionals transition to Google Cloud through this course?

Yes. The course helps experienced data professionals apply their existing knowledge to enterprise-scale cloud data engineering using Google Cloud services.