Google Cloud Professional Data Engineer Cert Prep (2025)

Course details

Embark on the path towards becoming a Google Professional Data Engineer through this certification course. Industry expert Noah Gift shows you how to design robust data processing systems using Google Cloud's top services like BigQuery and Cloud Functions. Explore the nuances of data storage technologies, including BigTable, Firestore, and Spanner, and make informed decisions based on business and data requirements. Discover prebuilt machine learning models, effective integration methods, and the fundamentals of ML pipeline deployment and monitoring. Gain insights into ensuring solution quality with advanced topics on scalability, flexibility, and security, leveraging Rust for secure and high-performance microservices. With an emphasis on hands-on learning and cutting-edge AI programming assistants, this course shows you how to build efficient, scalable, and secure data solutions that meet the needs of modern businesses.

Concepts

Introduction - Designing Data Processing Systems

Google Professional Data Engineer course overview
Onboard to GCP

Storage Technology Selection

Open-source vs. Google Cloud managed services
Pros and cons of open-source data engineering tools
Google Cloud analytics services

Data Pipeline Design

Data engineering pipelines
Google Cloud storage strategy

Data Warehousing and Processing Migration

Overview GCP storage
Optimize for GCP database solutions
Prompt engineering for BigQuery
Using Google BigQuery with Google Colab
Exploring data with Google BigQuery

Conclusion - Designing Data Processing Systems

Next steps

Introduction - Building and Operationalizing Data Processing Systems

Course overview

Storage System Implementation

Demo - Google Cloud Shell
Demo - Google Cloud Editor
Demo - Google CLI SDK
Demo - Google gcloud CLI tool
Storage comparison

Pipeline Building and Operationalization

Jack and the Beanstalk as a data pipeline
Compare compute offerings
Demo - Compute volatility on GCP

Processing Infrastructure Implementation

The challenges of big data
Demo - Extending GCP Cloud Functions
Data pipeline triggers

Conclusion - Building and Operationalizing Data Processing Systems

Next steps

Introduction - Operationalizing Machine Learning (ML) Models

Course overview

Pre-built ML Models as a Service

Google Colab with TensorFlow Hub
Using GCP NLP from the CLI

Training and Serving Infrastructure Selection

PyTorch pretrained model overview
Demo - PyTorch pretrained model
Understanding TPUs
TPUs as part of technology transition
Getting started with Vertex AI
Using GCP ML API vision from CLI

ML Model Measurement, Monitoring, and Troubleshooting

Plan-do-check-act methodology
Demo - Load testing with Locust
MLOps on GCP

Conclusion - Operationalizing Machine Learning Models

Using Google machine learning courses
Next steps

Introduction - Ensuring Solution Quality

Course overview

Security and Compliance Design

Integrated data security
Understand Rust crate audits by Google
The Rust language is secure by design

Scalability and Efficiency Assurance

Using Bard to enhance productivity
Copilot-enabled Rust
Continuous integration with Rust and GitHub actions
Demo unit test Rust
Energy efficiency of Python vs. Rust

Flexibility and Portability Assurance

What is distroless
Demo - Build and deploy Rust Microservice Cloud Run
Demo - App Engine Rust deploy

Conclusion - Ensuring Solution Quality

Next steps