Course Objectives
1. 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.
2. 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.
3. 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.
4. 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.
5. 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 experienced Google Cloud data engineers who have designed and implemented scalable data platforms, analytics solutions, and machine learning data pipelines for financial services, telecommunications, retail, and other enterprise organisations.
Backgrounds at the Top
Our Google Professional Data Engineer trainers have worked with enterprise cloud environments where Google Cloud technologies are used to process large volumes of data, build reliable data pipelines, manage data warehouses, and support advanced analytics and AI initiatives.
Clear & Effective Teaching
Google Cloud data engineering concepts, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, Cloud Composer, data pipeline design, data processing, data governance, security, and machine learning workflows are explained through practical enterprise use cases.
Hands-On Learning Focus
Students design and build data pipelines, work with BigQuery datasets, process batch and streaming data, integrate Google Cloud services, implement data transformation workflows, and develop scalable data solutions through structured hands-on cloud data engineering labs.
Up-to-Date Knowledge
Trainers keep the course content aligned with the latest Google Cloud Platform services, Professional Data Engineer certification objectives, modern data engineering practices, cloud-native architectures, and evolving technologies in data analytics and machine learning.
Earn an Industry-Recognized Google Professional Data Engineer Certification
Successfully completing the Google Professional Data Engineer (GCP) Course in Bangalore demonstrates your ability to design, build, secure, monitor, and optimize enterprise data platforms using Google Cloud.
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