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Google Cloud Professional Data Engineer Certification Training

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Multisoft Virtual Academy offers comprehensive online training for the Google Cloud Certified Professional Data Engineer Certification. This course provides a deep understanding of data processing systems, machine learning algorithms, and security protocols, enabling you to design, build, and maintain high-quality data solutions on Google Cloud. Experience hands-on learning with expert-led sessions and real-world projects.

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	19 Apr 2025	24	06:00 PM - 09:00 PM	Sat, Sun
	20 Apr 2025	24	06:00 PM - 09:00 PM	Sat, Sun

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Instructor-led Training Live Online Classes

	19 Apr 2025	24	06:00 PM - 09:00 PM	Sat, Sun
	20 Apr 2025	24	06:00 PM - 09:00 PM	Sat, Sun

Course Price At

$ 550

Enroll Now

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Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You’ll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

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Duration: 24 Hrs
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Google Cloud Professional Data Engineer Training Course Syllabus

Curriculum Designed by Experts

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The Google Cloud Certified Professional Data Engineer certification is aimed at data professionals who aim to demonstrate their ability to leverage Google Cloud technologies to process big data. These professionals design, build, operationalize, secure, and monitor data processing systems.

In an online training course for this certification, you can generally expect the following topics to be covered:

Google Cloud Platform Overview: Basic understanding of Google Cloud Platform, its services, and the purpose they serve.
Building and operationalizing data processing systems: This involves topics such as working with services like Pub/Sub, Dataflow, Bigtable, BigQuery, and understanding how to integrate them to build comprehensive data processing systems.
Operationalizing machine learning models: Understand the basics of AI Platform and how to use it for training and prediction.
Designing for reliability and security: Learn how to design secure, robust data processing systems with topics such as Identity and Access Management (IAM), Cloud Audit logs, etc.
Designing for scale and efficiency: Understanding how to design systems to handle increased load, and how to analyze and optimize performance.
Hands-on labs and case studies: Most good training courses will have a hands-on component where you can get practical experience working with Google Cloud services.

The Google Cloud Certified Professional Data Engineer Certification Online Training prepares individuals to harness the full potential of Google Cloud technologies for data-driven decision making. The course covers data processing, machine learning, data modeling, and ensures mastery of skills to design, build, and maintain data processing systems. Trainees also learn about operational efficiency and compliant use of data.

About This Course
Course Audience and Prerequisites
A Note About Demo Lessons

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Data Processing Concepts
Data Processing Pipelines

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Introduction to Data Storage in GCP
Working with Data
Cloud Storage
Service Accounts
Data Transfer Services
Cloud SQL
Demo: Creating a Cloud SQL Instance and Loading Data
Managing Google Cloud SQL Instances
Hands-On Lab
Working with Data in Google Cloud SQL
Hands-On Lab
Cloud Firestore
Exploring Cloud Firestore in Datastore Mode
Hands-On Lab
Cloud Run Data in GCS and Firestore
Hands-On Lab
Cloud Spanner
Demo: Working with Cloud Spanner
Setting Up for Google Cloud Spanner
Hands-On Lab
Cloud Memorystore
Demo: Working with Cloud Memorystore
Comparing Storage Options
Exam Tips

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MapReduce
Hadoop & HDFS
Apache Pig
Apache Spark
Apache Kafka

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Pub/Sub Concepts
Pub/Sub Basics
Demo: Working with Cloud Pub/Sub
Demo: Cloud Pub/Sub Client Libraries
Advanced Pub/Sub
Demo: Loosely Coupled Services with Cloud Pub/Sub
Demo: Stream Data through Cloud Pub/Sub to BigQuery
Exam Tips

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Dataflow Introduction
Pipeline Lifecycle
Dataflow Pipeline Concepts
Advanced Dataflow Concepts
Dataflow Security and Access
Using Dataflow
Exam Tips
Demo: Working with Cloud Dataflow
Demo: Streaming Pipelines with Cloud Dataflow
Create Streaming Data Pipeline on GCP with Cloud Pub/Sub, Dataflow, and BigQuery

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Dataproc Overview
Dataproc Basics
Demo: Working with Cloud Dataproc
Advanced Dataproc
Demo: Cloud Dataproc with the GCS Connector
Exam Tips

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Bigtable Concepts
Bigtable Architecture
Bigtable Data Model
Demo: Working with Cloud Bigtable
Bigtable Schema Design
Bigtable Advanced Concepts
Demo: Loading and Querying Data with Cloud Bigtable
Exam Tips

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BigQuery Basics
Using BigQuery
Partitioning and Clustering
Best Practices
Securing BigQuery
BigQuery Monitoring and Logging
Machine Learning with BigQuery ML
Exam Tips
Demo: Working with BigQuery
Demo: Advanced BigQuery Features

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Datalab Concepts
Demo: Working with Cloud Datalab
Demo: Jupyter Notebooks in GCP

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Reporting and Business Intelligence
Data Distributions
Introduction to Data Studio
Charts and Filters

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Cloud Composer Overview
Cloud Composer Architecture
Demo: Working with Cloud Composer
Advanced Cloud Composer

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Machine Learning Introduction
Machine Learning Basics
Machine Learning Types and Models
Overfitting
Hyperparameters
Feature Engineering

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Deep Learning with TensorFlow
Introduction to Artificial Neural Networks
Neural Network Architectures
Building a Neural Network

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Cloud AI Cloud APIs
Vision
Video Intelligence
Language
Conversation
Demo: Working with Cloud ML APIs

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Introduction to AutoML
Sight with AutoML
Language with AutoML
Structured Data with AutoML

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Introduction to Operationalizing ML Models
Kubeflow
AI Platform

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Security and Regulation Overview
IAM Best Practices
Data Security
Data Privacy
Industry Regulation

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Dataprep Overview
Demo: Working with Cloud Dataprep

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Reference Architectures: Big Data
Reference Architectures: Artificial Intelligence and Machine Learning
Reference Architectures: Internet of Things
Reference Architectures: Mobile & Gaming
External Resources and Tutorials
Exam Guide Breakdown
What to Expect From the Exam

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Google Cloud Professional Data Engineer Training Description

The objective of a Google Cloud Certified Professional Data Engineer certification online training course would typically be to provide learners with the knowledge, skills, and experience needed to effectively use Google Cloud technologies in designing, building, and managing robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

Key course objectives might include:

Understanding Google Cloud Platform (GCP): The course will aim to help participants fully understand GCP, its offerings, and its interface.
Fundamentals of Google Cloud Data Engineering: The course should cover all essential aspects of data engineering in the Google Cloud environment, such as designing, building, and operationalizing data processing systems.
Data Storage and Processing: Participants should learn how to work with and manage data storage and database services on GCP, including Bigtable, Firestore, Cloud SQL, Cloud Spanner, and BigQuery. They should also understand various data processing tools like Dataflow and Dataproc.
Machine Learning and Advanced Analytics: Learn how to use GCP tools for machine learning and data analytics, such as Cloud ML Engine, Data Studio, and AI Platform.
Security and Compliance: Understand the best practices for security and compliance in GCP, and how to ensure data privacy&

Data professionals
Individuals preparing for the Google Professional Data Engineer exam

To enroll in Google Cloud Certified Professional Data Engineer Certification Online Training, candidates must have:

Basic understanding of cloud computing: Familiarity with the core concepts of cloud technology, like computing power, storage, and networking in a cloud environment.
Experience with Google Cloud Platform (GCP): Candidates should have practical experience working with GCP. Knowledge of services like Google Compute Engine, Google Cloud Storage, and BigQuery is desirable.
Data handling skills: Understanding of databases, both SQL and NoSQL, and the ability to manipulate, analyze, and interpret complex data is necessary.
Programming Knowledge: Comfort with at least one programming language, preferably Python, is required to use Google Cloud's Data Engineering tools effectively.
Familiarity with machine learning concepts: Basic knowledge of machine learning and its applications can be beneficial in the data engineering context.
Working knowledge of Linux/Unix systems: Many cloud computing operations are carried out on Linux platforms, hence a basic understanding is helpful.

Google Cloud Professional Data Engineer Training Certification

Multisoft Virtual Academy provides a globally recognized training certificate to the participants, after successful completion of a training program. The training certificates are recognized and accepted across the world.

Multisoft Virtual Academy's training certificate comes with lifetime validity.

Aspirants can directly enroll for the desired course from the Book Now Button in the course page. You can also connect on Whatsapp at +91 8130666206 to talk with a training advisor. Multisoft Virtual Academy also offers customized training programs on a wide range of domains and skills.

All training programs offered by Multisoft Virtual Academy are delivered by certified industry experts, who have years of experience in the relevant domains. Multisoft Global Subject Matter Experts impart knowledge on a wide variety of training courses through one –on-one and corporate training sessions.

Multisoft Virtual Academy training certification can help participants stand out in the competitive job market. Since the training certificates are internationally accepted, participants can showcase their skills and knowledge to employers across the world.

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Google Cloud Professional Data Engineer Corporate Training Certification

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Google Cloud Professional Data Engineer Training FAQ's

Google data engineer certification exam comes with a set of difficult questions. So, it is not an easy certification to earn. If you want to earn this, you are suggested to go for Google Data Engineer – Professional Certification Training which is specially designed to help the certification aspirants.

You will get top priority in gaining the access to a job platform where you can easily apply to opportunities from Google with open jobs after you have earned Google certification, but there is no assurance that you will get hired by Google.

No, Google professional certifications are not provided for free. To get it, you will have to pay an amount to Google.

Obviously, yes! Google Cloud Platform is now holding the third position in the Cloud market after AWS and Azure. So, Google Cloud data engineer certification will add professional value to your career. However, it will depend on your career requirements whether he/she wants to get AWS, Azure, or Google Cloud Platform.