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Overview

This course is designed to help learners master the skills and knowledge required to become a Google Cloud Professional Data Engineer. You will learn how to design, build, secure, and operationalize data processing systems on Google Cloud, while preparing to pass the Professional Data Engineer certification exam with confidence.

Through a mix of conceptual lessons, hands-on labs, case studies, and exam-focused strategies, learners will gain the ability to manage end-to-end data solutions — from ingestion and transformation to storage, analytics, and machine learning integration.

What You Will Learn

By the end of this course, you will be able to:

1. Design and implement data processing systems that are secure, reliable, and scalable.

2. Ingest, process, and transform data using services like Pub/Sub, Dataflow, Dataproc, and Data Fusion.

3. Store and manage data using Google Cloud’s storage solutions, including BigQuery, Spanner, Bigtable, and Cloud Storage.

4. Prepare data for analytics and machine learning, enabling advanced use cases with BigQuery, Dataplex, and Vertex AI.

5. Optimize, automate, and monitor workloads, applying cost-saving strategies and fault tolerance mechanisms.

6. Apply exam strategies and practice with real-world scenarios to succeed in the Professional Data Engineer certification exam.

Course Modules

Module 1: Introduction to Google Cloud & Data Engineering

• Role of a Professional Data Engineer

• Certification overview & exam structure

• Core principles of data engineering

Module 2: Designing Data Processing Systems

• Security, compliance, and governance

• Reliability, fidelity, and disaster recovery

• Migration strategies and architecture design

Module 3: Ingesting & Processing Data

• Batch vs. streaming data pipelines

• Tools: Dataflow, Pub/Sub, Dataproc, Data Fusion, Kafka

• CI/CD and pipeline orchestration (Cloud Composer, Workflows)

Module 4: Storing & Managing Data

• Data warehouse design with BigQuery

• Data lakes with Dataplex and Cloud Storage

• Data mesh and federated governance

Module 5: Preparing Data for Analytics & ML

• Query optimization and BigQuery advanced features

• Data sharing with Analytics Hub

• Preparing datasets for ML pipelines (Vertex AI)

Module 6: Automating & Optimizing Workloads

• Monitoring and troubleshooting with Cloud Logging & Monitoring

• Automation with Composer DAGs and Workflows

• Resource optimization & cost management

Module 7: Exam Preparation & Practice

• Sample exam questions and scenario walkthroughs

• Study strategies & time management

• Final mock test with detailed feedback

Who This Course Is For

• Data engineers, analysts, and architects aspiring to become Google Cloud certified.

• Cloud professionals seeking to expand their expertise in data processing and analytics.

• Developers and IT specialists transitioning into data engineering roles.

• Learners preparing specifically for the Professional Data Engineer certification exam.