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Become a GCP Certified Data Engineer in Just 5 Days.

Google Cloud Certified - Professional Data Engineer

4800,-€ (excl. VAT)


5 Days


Upon completion, participants will understand how to build scalable, secure, and efficient data processing systems on GCP. You'll learn to deploy machine learning models, design for data portability, operationalize machine learning, and much more. You'll be equipped to tackle real-world data engineering challenges and make data-driven decisions.

Learning Objectives

While there are no formal prerequisites, it's recommended that you have:

  • Three years of industry experience, including experience with GCP.

  • One year of experience in areas such as SQL, data modeling, machine learning, or programming (e.g., Python).


This course is ideal for IT professionals, data analysts, and anyone aspiring to become a data engineer or enhance their expertise in GCP. Whether you're looking to validate your skills with a certification or seeking to implement advanced data solutions, this course provides the knowledge and hands-on experience you need.

Who Should Attend

Immerse yourself in the world of data engineering with NEOEDX's accelerated Google Cloud Certified Professional Data Engineer course. Over 5 days, you'll gain the skills to design and build robust data processing systems on the Google Cloud Platform (GCP), focusing on security, compliance, reliability, and flexibility. Learn to enable data-driven decision-making by collecting, transforming, and publishing data, and leverage machine learning models for real-time insights. This course is your fast track to becoming a proficient data engineer on GCP.

Course Description

  1. Introducing Google Cloud Platform: Overview and key components.

  2. Compute and Storage Fundamentals: Understanding core infrastructure.

  3. Data Analytics on the Cloud: Analyzing data using GCP tools.

  4. Scaling Data Analysis: Techniques for managing large datasets.

  5. Machine Learning: Basics and advanced machine learning with GCP.

  6. Data Processing Architectures: Building scalable data architectures.

  7. Google Cloud Dataproc Overview: Introduction and job execution.

  8. Integrating Dataproc with GCP: Enhancing data operations.

  9. Unstructured Data with Machine Learning APIs: Leveraging Google's ML APIs.

  10. Serverless Data Analysis with BigQuery: Utilizing BigQuery for analysis.

  11. Autoscaling Data Pipelines with Dataflow: Building and managing data pipelines.

  12. Tensorflow and CloudML: Building and scaling ML models.

  13. Feature Engineering: Preparing data for ML models.

  14. Streaming Analytics Pipeline: Architecture and implementation.

  15. Bigtable Applications: Leveraging Bigtable for high throughput and low-latency.

  16. Professional Data Engineer Certification: Understanding exam and certification.

  17. Case Studies and Review: Exam preparation with case studies and module reviews.

  18. Reliability, Policy, and Security Review: Ensuring secure and compliant data solutions.

Course Outline

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