Google Cloud Certified Professional Data Engineer Specialization

Launch Your Career as a Google Cloud Data Engineer.

Mastering Google Cloud Data Engineering

Instructor: Whizlabs Instructor

3 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

12 weeks to complete

at 3 hours a week

Flexible schedule

Learn at your own pace

3 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

12 weeks to complete

at 3 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Understand Google Cloud data engineering fundamentals, storage, and database services.
Analyze and manage data using BigQuery, data lakes, and analytics services.
Build and manage data processing, ETL, ingestion, and migration workflows.
Apply security, monitoring, optimization, and AI capabilities to Google Cloud data solutions.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

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Master a subject or tool with hands-on projects
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Specialization - 3 course series

GCP Professional Data Engineer is a comprehensive specialization designed to help learners build the knowledge and practical skills required to design, build, manage, and optimize data solutions on Google Cloud. The specialization covers Google Cloud fundamentals, data storage, databases, BigQuery, data processing, ETL, data lakes, data integration, data ingestion, migration, security, monitoring, and AI services.

The specialization consists of three courses:

Course 1: Google Cloud Foundations & Data Processing

Course 2: Google Cloud Storage, Databases & Analytics

Course 3: Google Cloud Operations, Security & AI

By completing this specialization, learners will be able to understand Google Cloud data engineering concepts, select appropriate data services, design data processing and analytics solutions, apply security and monitoring practices, and understand AI capabilities. The specialization is intended for aspiring data engineers, cloud professionals, developers, and learners preparing for the Google Cloud Professional Data Engineer certification.

Applied Learning Project

Learners will engage in hands-on projects that apply Google Cloud data engineering solutions to real-world scenarios, such as:

Designing and implementing data storage and processing solutions using Google Cloud services.
Building data pipelines using BigQuery, data ingestion, ETL, and data integration services.
Working with data lakes, databases, and analytics to manage and derive insights from large datasets.
Applying security, monitoring, migration, and AI services to support reliable and scalable data solutions.

These projects allow learners to apply core data engineering concepts in practical ways, building the skills needed to design, build, manage, and optimize data solutions on Google Cloud and prepare for the Google Cloud Professional Data Engineer certification.

Google Cloud Foundations & Data Processing

Course 1, 6 hours

What you'll learn

Google Cloud Foundations & Data Processing is designed to provide learners with a strong foundation in Google Cloud concepts and data engineering capabilities. The course introduces essential Google Cloud services and infrastructure concepts before progressing into data storage, database technologies, data processing, ETL, and workflow automation.

The course is divided into three modules, with each module further segmented into lessons and video lectures. Learners will develop an understanding of how Google Cloud services can be used to store, process, transform, protect, and manage data workloads through scalable cloud-based solutions. Course Modules: Module 1: Introduction to Google Cloud Module 2: Data Engineering Fundamentals Module 3: Data Processing & ETL By the end of this course, a learner will be able to: Understand Google Cloud fundamentals, services, regions, zones, and the Google Cloud Console. Identify appropriate Google Cloud storage and database solutions for different data workloads. Understand data processing and ETL concepts using Google Cloud services. Design and deploy data processing pipelines using services such as Cloud Dataflow and Cloud Dataproc. Apply data preparation, protection, lifecycle management, and workflow orchestration concepts using Google Cloud services. This course is intended for learners who want to build foundational knowledge of Google Cloud and develop practical understanding of data engineering, data processing, ETL, and workflow automation capabilities.

Skills you'll gain

Category: Google Cloud Platform

Category: Cloud Services

Category: Data Processing

Category: Extract, Transform, Load

Category: Cloud Infrastructure

Category: Data Transformation

Category: Data Pipelines

Category: Data Architecture

Category: Data Security

Category: Data Storage Technologies

Category: Cloud Management

Category: Cloud Storage

Category: Transaction Processing

Category: Dataflow

Category: Data Loss Prevention

Category: Database Systems

Category: Cloud Platforms

Category: Data Governance

Category: Databases

Category: Data Storage

Google Cloud Storage, Databases & Analytics

Course 2, 7 hours

What you'll learn

Google Cloud Storage, Databases & Analytics is designed to provide learners with a strong understanding of Google Cloud storage, data lakes, managed database services, data warehousing, and analytics capabilities. The course focuses on how Google Cloud services can be used to securely store, manage, optimize, and analyze data at scale.

The course is divided into three modules, with each module further segmented into lessons and video lectures. Learners will explore cloud storage and data lake architectures, evaluate managed database solutions for different workloads, and use BigQuery for scalable data warehousing and analytics. Course Modules: Module 1: Cloud Storage & Data Lakes Module 2: Managed Database Services Module 3: BigQuery & Data Analytics By the end of this course, a learner will be able to: Understand Google Cloud Storage and data lake concepts, including data discovery, security, monitoring, and cost optimization. Evaluate and select appropriate managed database services based on workload, connectivity, scalability, performance, and cost requirements. Optimize database solutions across services such as Cloud SQL, Cloud Spanner, AlloyDB, Bigtable, and Firestore. Understand BigQuery capabilities for cloud data warehousing, analytics, monitoring, and query processing. Analyze and visualize data using BigQuery and Looker Studio while understanding data warehouse migration and hybrid multi-cloud concepts. This course is intended for learners who want to develop practical knowledge of Google Cloud storage, databases, data lakes, data warehousing, and analytics and build the skills required to work with modern cloud data solutions.

Skills you'll gain

Category: Data Warehousing

Category: Capacity Management

Category: Database Management

Category: Cloud Storage

Category: Looker (Software)

Category: Data Lakes

Category: Google Cloud Platform

Category: Scalability

Category: Data Governance

Category: Databases

Category: Data Architecture

Category: Hybrid Cloud Computing

Category: Redis

Category: Performance Tuning

Category: Data Management

Category: Data Access

Category: Business Intelligence

Category: Data Storage

Google Cloud Operations, Security & AI

Course 3, 4 hours

What you'll learn

Google Cloud Operations, Security & AI is designed to provide learners with a strong understanding of Google Cloud data ingestion, migration, cloud operations, security, monitoring, and artificial intelligence services. The course focuses on how Google Cloud capabilities can be used to build reliable data pipelines, migrate workloads, secure cloud environments, monitor operations, and integrate AI and machine learning into modern cloud solutions.

The course is divided into three modules, with each module further segmented into lessons and video lectures. Learners will explore streaming data pipelines, migration strategies, DevOps and CI/CD, cloud security and governance, observability, encryption, and Google Cloud AI and machine learning services. Course Modules: Module 1: Data Ingestion & Migration Module 2: Operations, Security & Monitoring Module 3: AI & Machine Learning Services By the end of this course, a learner will be able to: Plan and build streaming data pipelines and understand data ingestion approaches on Google Cloud. Apply migration planning, Database Migration Service, and DevOps and CI/CD concepts to cloud solutions. Understand Google Cloud security, compliance, IAM, observability, encryption, and key management capabilities. Use Google Cloud AI APIs for speech, vision, and language translation use cases. Understand AI and machine learning model development and training using Google Cloud AI services. This course is intended for learners who want to develop practical knowledge of Google Cloud operations, security, data migration, and AI capabilities and build the skills required to manage secure, reliable, and intelligent cloud solutions.