Google Professional Data Engineer Certification - Course Overview

Google Professional Data Engineers enable data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.


After Completing Google Professional Data Engineer Certification Course, Students Will be Able To:

  • Acquire knowledge on cloud computing basics and how Google Cloud products and services can be used to achieve an organization’s goals.
  • Attain skills required to clear the GCP Cloud Digital Leader Certification Exam.

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Main Highlights of GCP Data Engineer Course

  • Our Google Cloud Professional Data Engineer training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • Industry certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.
  • Being an authorized GCP Learning Partner gives us the edge over competition.

Who can take up a Google Cloud Data Engineer Training ?

  • IT professionals who work with data and have experience with data engineering tasks can benefit from this training.
  • For those who want to work in data engineering and want to demonstrate their skills and knowledge can take up this training.
  • Professionals who are already working in data engineering roles and want to enhance their skills and advance their career can take up this training.
  • Individuals who have experience working with GCP data engineering services or related technologies can take up this training to deepen their knowledge and skills.
  • IT managers who want to learn more about GCP data engineering services and how they can be used to improve their organization's data management and analysis can take up this training
  • Anyone who wants to gain a comprehensive understanding of GCP data engineering services, data modeling, data warehousing, data ingestion, and data processing can take up this training.


  • Google Cloud Associate Engineer certification.
  • Salient Google Cloud Professional Data Engineer Certification Training Benefits

    • Comprehensive coverage: Google cloud data engineer training covers all the topics needed to pass the Google Certified Professional Data Engineer exam, including GCP data engineering services, data modeling, data warehousing, data ingestion, and data processing.
    • Hands-on experience: Google data engineer course includes hands-on labs and projects that allow you to gain practical experience with GCP services and data engineering tasks.
    • Expert instructors: The training is delivered by experienced instructors who are certified Google Cloud Professionals and have real-world experience in data engineering.
    • Flexible learning options: Google cloud certified data engineer is available in various formats, including instructor-led online classes, on-demand videos, and self-paced e-learning courses, allowing you to choose the course of study that most closely matches your schedule and learning style.
    • Exam preparation: The training provides exam preparation materials, including practice exams, exam tips, and study guides, will assist you in getting ready for the Google Certified Professional Data Engineer exam.
    • Career advancement: Earning the Google Certified Professional Data Engineer certification can enhance your career prospects and demonstrate your expertise in GCP data engineering to potential employers.
    • Recognition: The Google Certified Professional Data Engineer certification is recognized globally as a validation of your skills and expertise in data engineering.
    • Networking opportunities: The training provides opportunities to connect with other data engineering professionals and learn from their experiences and perspectives.
    • Access to GCP resources: As a Google Certified Professional Data Engineer, you will have access to GCP resources and support to help you keep abreast with new breakthroughs in data engineering.
    • Ongoing learning: The training provides a foundation for ongoing learning and development in data engineering, helping you to stay competitive in a rapidly evolving industry

    Google Professional Data Engineer Training Roadmap

    • Gain foundational knowledge: Start with learning the basics of data engineering, including data modeling, data warehousing, data ingestion, and data processing.
    • Learn GCP services: Next, learn about the GCP services that are relevant to data engineering, such as BigQuery, Cloud Storage, Cloud Dataproc, and Cloud Pub/Sub.
    • Hands-on experience: Gain hands-on experience with GCP services by completing projects and labs that focus on data engineering tasks such as building data pipelines, optimizing queries, and designing data models.
    • Study for the exam: Use study materials and practice exams to prepare for the Google Certified Professional Data Engineer exam, which tests your knowledge of GCP data engineering services, best practices, and troubleshooting techniques.
    • Take the exam: Schedule and take the Google Certified Professional Data Engineer exam, which is a timed, multiple-choice exam that assesses your ability to design, build, operationalize, and secure data processing systems using GCP.
    • Achieve certification: Upon passing the exam, you will earn the Google Certified Professional Data Engineer Certificate, which demonstrates your knowledge and expertise in GCP data engineering.
    • Keep up-to-date: As technology evolves and new GCP services are released, it is important to stay up-to-date with the latest developments. Utilise the options for continued training and certification to keep your skills current and relevant.

    Course Outline Download Course Outline

    • Designing Data Processing Systems
    • Building and Operationalizing Data Processing Systems
    • Operationalizing Machine Learning Models
    • Security, Policy, and Reliability

    • Introduction
    • Big Data and Machine Learning on Google Cloud
    • Data Engineering for Streaming Data
    • Big Data with BigQuery
    • Machine Learning Options on Google Cloud
    • The Machine Learning Workflow with Vertex AI


    • Vertex AI: Qwik Start
    • Exploring a BigQuery Public Dataset
    • Vertex AI: Predicting Loan Risk with AutoML

    • Introduction
    • Introduction to Data Engineering
    • Building a Data Lake
    • Building a Data Warehouse


    • BigQuery: Qwik Start - Command Line
    • Creating a Data Warehouse Through Joins and Unions
    • Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors

    • Introduction to Building Batch Data Pipelines
    • Executing Spark on Dataproc
    • Serverless Data Processing with Dataflow
    • Manage Data Pipelines with Cloud Data Fusion and Cloud Composer


    • Dataflow: Qwik Start - Templates
    • Dataflow: Qwik Start - Python
    • Dataproc: Qwik Start - Console
    • Cloud Composer: Copying BigQuery Tables Across Different Locations

    • Introduction to Processing Streaming Data
    • Serverless Messaging with Pub/Sub
    • Dataflow Streaming Features
    • High-Throughput BigQuery and Bigtable Streaming Features
    • Advanced BigQuery Functionality and Performance


    • Building an IoT Analytics Pipeline on Google Cloud
    • ETL Processing on Google Cloud Using Dataflow and BigQuery
    • Creating Date-Partitioned Tables in BigQuery
    • Troubleshooting and Solving Data Join Pitfalls
    • Working with JSON, Arrays, and Structs in BigQuery

    • Introduction to Analytics and AI
    • Prebuilt ML Model APIs for Unstructured Data
    • Big Data Analytics with Notebooks
    • Production ML Pipelines with Kubeflow
    • Custom Model building with SQL in BigQuery ML
    • Custom Model Building with AutoML


    • Dataprep: Qwik Start
    • Creating a Data Transformation Pipeline with Cloud Dataprep
    • Predict Visitor Purchases with a Classification Model in BQML
    • Cloud Natural Language API: Qwik Start
    • Google Cloud Speech API: Qwik Start
    • Video Intelligence: Qwik Start


    • By earning GCP Data Engineer certification, you can be competent Google certified professional.
    • Demonstrate skills and knowledge to build, monitor, and tune high performance data engineering systems.
    • On successful completion of Google Data Engineer course, aspirants receive a Course Completion Certificate from us.
    • By successfully clearing the Google Data Engineer exam, aspirants earn Google Certification.

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    Course ID: 13599

    Course Price at

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