GCP – Google Cloud Professional Data Engineer Certification

GCP – Google Cloud Professional Data Engineer Certification — Course Overview & Key Highlights (2025)

Course introduction

GCP – Google Cloud Professional Data Engineer Certification is a comprehensive, hands-on training designed to help learners master data engineering on Google Cloud Platform and confidently prepare for the Professional Data Engineer (PDE) certification.

Created by Ankit Mistry, this course focuses on real-world data pipelines, storage systems, databases, BigQuery, streaming, machine learning, and analytics on GCP, with over 80+ hands-on demos across core Google Cloud data services.

Course details

• Course title: GCP – Google Cloud Professional Data Engineer Certification
• Instructor: Ankit Mistry
• Language: English
• Last updated: November 2025
• Duration: ~32.5 hours (32h 33m total)
• Rating: ★4.4 / 5
• Learners: 39,441+
• Price (typical sale): ~US$6–9 (varies by Udemy promotions)
• Access: Full lifetime access (mobile + TV)
• Certificate: Udemy Certificate of Completion included

Key highlights

• End-to-end Google Cloud Data Engineering training
• Covers PDE certification objectives in depth
• 80+ hands-on labs and demos
• Learn batch & streaming data pipelines on GCP
• Strong focus on BigQuery, Dataflow, Dataproc & Pub/Sub
• Includes ML, AutoML & prebuilt AI APIs
• Practical exposure to real production-style architectures

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What you will learn

Data Engineering Foundations

• Core data engineering & database concepts
• Designing scalable and reliable data solutions

GCP Infrastructure & Compute

• Provision VMs, GKE, Cloud Run, App Engine
• Container-based and serverless architectures

Storage & Databases

• Cloud Storage, Persistent Disks, Filestore (unstructured data)
• SQL databases, Cloud Spanner, and BigQuery
• BigTable & Datastore for semi-structured data
• Memorystore (Redis) for in-memory performance

Data Processing & Pipelines

• Build pipelines using Dataflow (Apache Beam)
• Batch processing with Dataproc (Hadoop & Spark)
• Data integration with Data Fusion
• Workflow orchestration using Composer (Airflow)

Analytics & Visualization

• Petabyte-scale analytics with BigQuery
• Create dashboards with Google Data Studio
• Search datasets using Data Catalog

Machine Learning & AI on GCP

• ML fundamentals and GCP ML services
• Use prebuilt ML APIs (Vision, Language, Speech)
• Apply AutoML on custom datasets
• Build custom ML models using Notebooks & Scikit-learn
• Deploy Scikit-learn & TensorFlow models as endpoints

Security & Data Governance

• Detect sensitive data using DLP API
• Asynchronous messaging with Cloud Pub/Sub

 Frequently asked questions (FAQ)

Q — Is this course aligned with the Professional Data Engineer exam?
A — Yes. The curriculum is designed around PDE certification objectives with practical labs.

Q — Do I need prior GCP experience?
A — No prior GCP experience is required, but basic cloud curiosity helps.

Q — Is hands-on practice included?
A — Yes. The course includes 80+ practical demos and labs.

Q — Do I need a Google Cloud account?
A — Yes. A GCP account with debit/credit card is required.

Q — Will I get a certificate?
A — Yes. You receive a Udemy Certificate of Completion.

Why this course is worth it

Unlike theory-heavy certification courses, this training focuses on real data engineering workflows used in production on Google Cloud. It prepares you not only for the exam, but also for actual data engineer job roles.

Final verdict

If your goal is to become a Google Cloud Data Engineer or pass the Professional Data Engineer certification, this course offers strong hands-on depth across storage, pipelines, analytics, and ML. It’s practical, certification-aligned, and job-focused.