Google Professional Data Engineer

The Google Professional Data Engineer certification validates your ability to design, build, operationalize, secure, and monitor data processing systems on Google Cloud. It covers the full data engineering lifecycle — from ingestion and storage to processing, analysis, and machine learning. This certification is highly valued by data engineers, analytics engineers, and ML engineers who work with GCP's powerful data stack including BigQuery, Dataflow, Pub/Sub, Vertex AI, and Looker. It demonstrates mastery of building scalable, reliable, and cost-effective data pipelines and analytical systems.

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What is the Google Professional Data Engineer Certification?

Who Should Take This Course?

Data engineers designing and building data pipelines on GCP
Analytics engineers working with BigQuery and Looker
Machine learning engineers building data-driven ML solutions
Data architects designing enterprise data platforms
ETL developers migrating on-premise pipelines to Google Cloud
Database administrators modernizing to cloud-native data stores
BI developers building dashboards powered by GCP data products

What You Will Learn in the PDE Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Designing Data Processing Systems

Design scalable data ingestion, storage, and processing architectures.

Selecting storage systems: BigQuery, Bigtable, Cloud SQL, Spanner, Firestore
Batch vs. streaming data processing patterns
Data lake architecture with Cloud Storage and BigQuery
Data mesh and federated query architectures

Ingesting and Processing Data

Build reliable data pipelines using Dataflow, Pub/Sub, and Dataproc.

Apache Beam pipelines with Cloud Dataflow
Pub/Sub for real-time event streaming
Dataproc for Spark and Hadoop workloads
Cloud Data Fusion for no-code ETL pipelines

Storing and Analyzing Data

Store, query, and analyze data at scale with BigQuery and other analytics tools.

BigQuery internals: partitioning, clustering, materialized views
BigQuery ML for in-database machine learning
Looker and Looker Studio for visualization and reporting
Data Catalog for metadata management and data discovery

Preparing and Using Data for Machine Learning

Prepare data and build ML models using Vertex AI and related services.

Feature engineering with Vertex AI Feature Store
Vertex AI Pipelines for MLOps automation
AutoML and custom model training on Vertex AI
Model evaluation, monitoring, and retraining strategies

Maintaining and Automating Data Workloads

Operationalize and monitor data systems for reliability and performance.

Cloud Composer (Apache Airflow) for workflow orchestration
Monitoring data pipelines with Cloud Monitoring and Logging
Cost optimization: BigQuery slots, flat-rate pricing, reservations
Data security: column-level encryption, VPC Service Controls, DLP

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

Associate Cloud Engineer or equivalent practical GCP knowledge
2+ years of experience in data engineering or analytics
Proficiency in SQL, Python, or Java for data pipeline development
Understanding of distributed computing concepts (Spark, Hadoop)
Familiarity with BigQuery, Pub/Sub, and Cloud Storage

Exam Information

Everything you need to know about the PDE certification exam.

Exam Component	Details
Exam Name	Google Professional Data Engineer
Exam Code	PDE
Exam Type	Multiple Choice and Multiple Select
Total Questions	60
Passing Score	Approximately 70%
Exam Duration	120 minutes
Language	English, Japanese
Exam Provider	Google Cloud / Kryterion (online proctored or test center)
Exam Focus	Designing, building, and maintaining data processing systems and machine learning pipelines on GCP
Exam Registration	Register at cloud.google.com/certification via Kryterion Webassessor portal
Retake Policy	14-day wait after first failure; 60-day wait after second; 365-day wait after third
Certification Validity	3 years (recertification required)