Connect with us:

Join us for a FREE hands-on Meetup webinar on Text Analysis with Azure AI Language Service (AI102) | Friday, December 20th, 2024 · 5:00 PM IST/ 7:30 AM EST Join us for a FREE hands-on Meetup webinar on Text Analysis with Azure AI Language Service (AI102) | Friday, December 20th, 2024 · 5:00 PM IST/ 7:30 AM EST

Google Cloud Professional Data Engineer Certification Preparation

Design, Build, and Optimize Scalable Data Solutions on Google Cloud Platform

Duration

4 Days (8 hours per day)

Level

Intermediate Level

Design and Tailor this course

As per your team needs

Edit Content

The Google Cloud Professional Data Engineer certification prep course is designed to equip learners with the knowledge and skills required to design, build, operationalize, secure, and monitor data processing systems on Google Cloud Platform (GCP). This course emphasizes key concepts like data pipeline design, data modeling, and ensuring solution scalability and reliability.

By the end of the course, participants will have a strong foundation to take the Google Cloud Professional Data Engineer certification exam.

Edit Content

Edit Content

Introduction to the Certification

Overview of Google Cloud Professional Data Engineer certification
Exam format, objectives, and preparation tips
Understanding the exam blueprint

GCP Fundamentals for Data Engineering

Introduction to GCP services and tools
Navigating the GCP Console
Understanding IAM and security basics

Designing Data Processing Systems

Architecting batch and stream data pipelines
Designing for scalability, reliability, and high performance
Choosing the right GCP services for data processing (Dataflow, Dataproc)

Data Storage Solutions

Exploring Cloud Storage, BigQuery, and Cloud SQL
Optimizing data storage for cost and performance
Implementing data partitioning and clustering

Data Transformation and ETL Workflows

Building ETL pipelines using Dataflow
Introduction to Apache Beam
Automating workflows with Cloud Composer

Data Modeling and Schema Design

Principles of data modeling for relational and NoSQL databases
Best practices for designing schemas in BigQuery
Performance tuning for query optimization

Implementing Machine Learning on GCP

Introduction to AI and ML services on GCP
Using AI Platform and Vertex AI for model training and deployment
Integrating ML into data pipelines

Monitoring and Troubleshooting Data Pipelines

Monitoring solutions with Stackdriver
Debugging and troubleshooting pipeline issues
Setting up alerts and automated responses

Security and Compliance in Data Engineering

Securing data at rest and in transit
Implementing access control with IAM
Compliance considerations (GDPR, HIPAA, etc.)

Preparing for the Exam

Review of exam topics and key focus areas
Practice questions and mock exams
Time management strategies during the exam
Tips for success from certified professionals

Edit Content

Stay ahead with DataCouch! Your partner in mastering the latest advancements in AI, Data Science, DevOps, and more.

Quick Links

our Offerings

Get in touch

Sign up for DataCouch Communications

Copyright 2025 © DataCouch