Google Professional Data Engineer Certification - Course Overview

This course provides learners with hands-on experience in designing and building modern data engineering systems on Google Cloud. Through lectures, demos, labs, and classroom activities, participants will learn how to design scalable data architectures, build batch and streaming pipelines, implement lakehouse solutions, automate workflows, and integrate AI/ML capabilities into data platforms. 

The course covers structured, unstructured, and streaming data processing using Google Cloud data engineering products and modern data architecture principles. 

 

After Completing Google Professional Data Engineer Certification Course, Students Will be Able To:

  • Design scalable and secure data processing systems on Google Cloud.
  • Build and manage batch and streaming data pipelines.
  • Implement modern data lakehouse architectures.
  • Automate orchestration and workflow management.
  • Use BigQuery, Dataflow, Dataproc, Pub/Sub, and Bigtable effectively.
  • Apply AI/ML and analytics capabilities to data engineering workflows.
  • Implement monitoring, governance, and observability practices.
  • Build operational and analytical data platforms using Google Cloud services.

Upcoming Batches

Loading Dates...

Main Highlights of GCP Data Engineer Course

  • Modern Data Engineering Foundations

  • Lakehouse and Data Warehouse Architectures

  • Batch and Streaming Data Pipelines

  • Automation and Orchestration

  • Data Quality, Governance, and Security

  • AI/ML and Advanced Analytics Integration

  • Observability and Monitoring

  • Real-World Hands-On Labs and Use Cases 

Who should attend Google Cloud Data Engineer Training ?

  • Data Engineers
  • Data Analysts
  • Data Architects

Prerequisites of Google Professional Data Engineer Certification

Prior knowledge gained through the following courses will be beneficial: 

Salient Google Cloud Professional Data Engineer Certification Training Benefits

  • Comprehensive coverage: Google cloud data engineer training covers all the topics needed to pass the Google Certified Professional Data Engineer exam, including GCP data engineering services, data modeling, data warehousing, data ingestion, and data processing.
  • Hands-on experience: Google data engineer course includes hands-on labs and projects that allow you to gain practical experience with GCP services and data engineering tasks.
  • Expert instructors: The training is delivered by experienced instructors who are certified Google Cloud Professionals and have real-world experience in data engineering.
  • Flexible learning options: Google cloud certified data engineer is available in various formats, including instructor-led online classes, on-demand videos, and self-paced e-learning courses, allowing you to choose the course of study that most closely matches your schedule and learning style.
  • Exam preparation: The training provides exam preparation materials, including practice exams, exam tips, and study guides, will assist you in getting ready for the Google Certified Professional Data Engineer exam.
  • Career advancement: Earning the Google Certified Professional Data Engineer certification can enhance your career prospects and demonstrate your expertise in GCP data engineering to potential employers.
  • Recognition: The Google Certified Professional Data Engineer certification is recognized globally as a validation of your skills and expertise in data engineering.
  • Networking opportunities: The training provides opportunities to connect with other data engineering professionals and learn from their experiences and perspectives.
  • Access to GCP resources: As a Google Certified Professional Data Engineer, you will have access to GCP resources and support to help you keep abreast with new breakthroughs in data engineering.
  • Ongoing learning: The training provides a foundation for ongoing learning and development in data engineering, helping you to stay competitive in a rapidly evolving industry

Google Professional Data Engineer Training Roadmap

  • Gain foundational knowledge: Start with learning the basics of data engineering, including data modeling, data warehousing, data ingestion, and data processing.
  • Learn GCP services: Next, learn about the GCP services that are relevant to data engineering, such as BigQuery, Cloud Storage, Cloud Dataproc, and Cloud Pub/Sub.
  • Hands-on experience: Gain hands-on experience with GCP services by completing projects and labs that focus on data engineering tasks such as building data pipelines, optimizing queries, and designing data models.
  • Study for the exam: Use study materials and practice exams to prepare for the Google Certified Professional Data Engineer exam, which tests your knowledge of GCP data engineering services, best practices, and troubleshooting techniques.
  • Take the exam: Schedule and take the Google Certified Professional Data Engineer exam, which is a timed, multiple-choice exam that assesses your ability to design, build, operationalize, and secure data processing systems using GCP.
  • Achieve certification: Upon passing the exam, you will earn the Google Certified Professional Data Engineer Certificate, which demonstrates your knowledge and expertise in GCP data engineering.
  • Keep up-to-date: As technology evolves and new GCP services are released, it is important to stay up-to-date with the latest developments. Utilise the options for continued training and certification to keep your skills current and relevant.

Course outline for CloudThat Course Completion Certificate will be awarded to all learners who complete the training Download Course Outline

Topics

  • Role of a Data Engineer
  • Data Sources and Data Sinks
  • Data Formats
  • Storage Solutions
  • Metadata Management
  • Analytics Hub

Learning Outcomes

  • Understand core data engineering responsibilities.
  • Compare storage and metadata management solutions.
  • Share datasets using Analytics Hub.

Activities

  • Lab: Loading Data into BigQuery
  • Quiz

Topics

  • Replication and Migration Architecture
  • gcloud CLI
  • Storage Transfer Service
  • Transfer Appliance
  • Datastream

Learning Outcomes

  • Design data replication and migration strategies.
  • Use Datastream and transfer services effectively.

Activities

  • Migration Architecture Discussion
  • Quiz

Topics

  • Extract and Load Architecture
  • bq Command-Line Tool
  • BigQuery Data Transfer Service
  • BigLake

Learning Outcomes

  • Build extract-and-load pipelines.
  • Use BigLake for non-extract-load patterns.

Activities

  • Lab: BigLake Qwik Start
  • Quiz

Topics

  • ELT Architecture
  • BigQuery SQL Scripting
  • Scheduling
  • Dataform

Learning Outcomes

  • Build ELT workflows using BigQuery and Dataform.
  • Automate SQL transformations and scheduling.

Activities

  • Lab: Create and Execute a SQL Workflow in Dataform
  • Quiz

Topics

  • ETL Architecture
  • GUI Tools for ETL
  • Dataproc
  • Streaming Processing
  • Bigtable Pipelines

Learning Outcomes

  • Design ETL pipelines using Dataproc and Dataflow.
  • Implement batch and streaming processing architectures

Activities

  • Lab: Dataproc Serverless for Spark
  • Lab: Streaming Dashboard with Dataflow
  • Quiz

Topics

  • Automation Patterns
  • Cloud Scheduler
  • Workflows
  • Cloud Composer
  • Cloud Run Functions
  • Eventarc

Learning Outcomes

  • Automate and orchestrate data workflows.
  • Implement event-driven automation solutions.

Activities

  • Lab: Use Cloud Run Functions to Load BigQuery
  • Quiz

Topics

  • Data Lakes
  • Data Warehouses
  • Data Lakehouse
  • Architecture Selection

Learning Outcomes

  • Compare modern data architecture patterns.
  • Evaluate lakehouse benefits and trade-offs.

Activities

  • Architecture Comparison Workshop
  • Quiz

Topics

  • Cloud Storage Foundation
  • Apache Iceberg
  • BigQuery
  • AlloyDB
  • Federated Queries

Learning Outcomes

  • Build unified lakehouse architectures.
  • Combine operational and analytical data.

Activities

  • Lab: Federated Query with BigQuery
  • Quiz

Topics

  • BigQuery Fundamentals
  • Partitioning and Clustering
  • BigLake
  • External Tables

Learning Outcomes

  • Build scalable cloud data warehouses.
  • Use BigLake and external tables effectively.

Activities

  • Lab: Querying External Data and Iceberg Tables
  • Quiz

Topics

  • Governance and Security
  • Data Loss Prevention
  • Analytics and Machine Learning
  • Migration Strategies

Learning Outcomes

  • Implement governance and metadata management.
  • Enable advanced analytics and AI workloads.

Activities

  • Lab: BigQuery ML
  • Lab: Vector Search with BigQuery

Topics

  • Batch Pipeline Use Cases
  • Processing Challenges

Learning Outcomes

  • Identify when to use batch processing.
  • Analyze common pipeline challenges.

Activities

  • Batch Processing Discussion
  • Quiz

Topics

  • Batch Pipeline Design
  • Large-Scale Transformations
  • Dataflow
  • Serverless Spark
  • Orchestration

Learning Outcomes

  • Build scalable batch pipelines.
  • Optimize throughput and cost efficiency.

Activities

  • Lab: Batch Pipeline with Spark
  • Lab: Dataflow Job Builder UI
  • Quiz

Topics

  • Validation and Cleansing
  • Error Analysis
  • Schema Evolution
  • Deduplication

Learning Outcomes

  • Ensure pipeline data quality and consistency.
  • Handle schema changes and duplicate data.

Activities

  • Lab: Validate Data Quality in Batch Pipelines
  • Quiz

Topics

  • Cloud Composer
  • Observability
  • Alerts and Troubleshooting
  • Visual Pipeline Management

Learning Outcomes

  • Orchestrate complex workflows.
  • Implement monitoring and troubleshooting practices.

Activities

  • Lab: Batch Pipelines in Cloud Data Fusion
  • Quiz

Topics

  • Streaming Pipeline Concepts
  • Business Use Case
  • Challenges and Mission

Learning Outcomes

  • Understand streaming pipeline requirements.
  • Identify streaming architecture challenges.

Activities

  • Use Case Discussion
  • Architecture Overview

Topics

  • Streaming ETL
  • Streaming AI/ML
  • Streaming Applications
  • Reverse ETL

Learning Outcomes

  • Design streaming architectures for different use cases.
  • Compare streaming pipeline patterns.

Activities

  • Streaming Architecture Workshop
  • Quiz

Topics

  • Pub/Sub
  • Managed Service for Apache Kafka
  • Dataflow
  • Streaming Processing

Learning Outcomes

  • Select appropriate streaming technologies.
  • Build real-time streaming pipelines.

Activities

  • Lab: Stream Data Pipelines – Esports Use Case
  • Quiz

Topics

  • BigQuery Streaming
  • Continuous Queries
  • Reverse ETL
  • Bigtable Integration

Learning Outcomes

  • Build streaming analytics solutions.
  • Integrate operational and analytical streaming systems.

Activities

  • Lab: Apache Beam and Bigtable Integration 
  • Lab: Pub/Sub and BigQuery Streaming
  • Quiz

Topics

  • Course Summary
  • Next Steps

Learning Outcomes

  • Consolidate modern data engineering concepts.
  • Review best practices and architecture patterns.

Activities

  • Final Review Session
  • Q&A Discussion

Select Course date

Loading Dates...
Add to Wishlist

Course ID: 13600

Course Price at

Loading price info...
Enroll Now

This course is designed for data engineers, analysts, and architects building modern data platforms on Google Cloud.

Yes. The course includes multiple labs, architecture workshops, pipeline exercises, and real-world streaming scenarios.

The course covers BigQuery, Dataflow, Dataproc, Pub/Sub, Bigtable, BigLake, Vertex AI, and several Google Cloud data services.

Yes. Streaming ETL, streaming analytics, reverse ETL, Pub/Sub, Kafka, and Dataflow are major focus areas.

Yes. Learners will explore modern lakehouse architectures using BigLake, BigQuery, and Apache Iceberg.

The course is available in Instructor-Led Training (ILT) and On-Demand formats.

Yes. SQL proficiency and familiarity with Python are recommended.

Yes. A CloudThat Course Completion Certificate will be awarded after successful completion of the training.

Enquire Now