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Google Cloud Certified Professional Data Engineer

Course Summary

Google Cloud Certifications are the world’s most prestigious certifications. Google Cloud Platform is ranked as the world’s third largest cloud provider. The world-renowned services such as Google Search and Youtube are also hosted on the Google Cloud, which means applications which will be hosted on GCP will share the same world class infrastructure used by other Google products. 


Course Objectives 

After completing this Google Cloud Certification training, you should be able to:

  • Understand Google Cloud Platform concepts and terminologies
  • Understand GCP products and services
  • Learn methods to develop, implement, and deploy solutions
  • Distinguish between features of similar or related products and technologies
  • Learn to work with the Compute Engine, Cloud Storage, Cloud SQL and Big Query GCP services
  • Manage GCP environments using three different methods (Console, App, Command-Line)
  • Develop knowledge of solution patterns -- methods, technologies, and designs that are used to implement security, scalability, high availability, and other desired qualities
  • Understand basics of Big Data and Machine Learning services of GCP

Course Outline

Introducing Google Cloud Platform

Google Cloud Platform offers four main kinds of services: Compute, Storage, Big Data, and Machine Learning. This course focuses mostly on the first two, together with Google Virtual Private Cloud (VPC) networking. This module orients learners to the basics of Google Cloud Platform. It traces the evolution of cloud computing and explains what is unique about Google's approach to it. The module introduces the key structural concepts of regions and zones

Getting Started with Google Cloud Platform

GCP customers use projects to organize the resources they use. They use Google Cloud Identity and Access Management, also called “IAM,” to control who can do what with those resources. They use any of several technologies to connect with GCP. This module covers each of these topics, and it introduces a service called Cloud Launcher that is an easy way to get started with GCP.

Virtual Machines in the Cloud

Compute Engine lets you run virtual machines on Google’s global infrastructure. This module covers how Compute Engine works, with a focus on Google virtual networking. Every application needs to store data. Different applications and workloads require different storage and database solutions. This module describes and differentiates among GCP's core storage options: Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Datastore, and Google Bigtable. 

Containers in the Cloud

Containers are simple and interoperable, and they enable seamless, fine-grained scaling. Kubernetes is an orchestration layer for containers. Kubernetes Engine is Kubernetes as a service, a scalable managed offering that runs on Google’s infrastructure. You direct the creation of a cluster, and Kubernetes Engine schedules your containers into the cluster and manages them automatically, based on requirements you define. This module explains how Kubernetes Engine works and how it helps deploy applications in containers.

Applications in the Cloud

App Engine is a Platform-as-a-Service ("PaaS") offering. The App Engine platform manages the hardware and networking infrastructure required to run your code. App Engine provides built-in services that many web applications need. This module describes how App Engine works.

Developing, Deploying and Monitoring in the Cloud

Popular tools for development, deployment, and monitoring just work in GCP. Customers also have options for tools in each of these three areas that are tightly integrated with GCP. This module covers those tools.

Big Data and Machine Learning in the Cloud

GCP's big-data and machine learning offerings are intended to help customers get the most out of data. These tools are intended to be simple and practical to embed in your applications. This module describes the available big-data and machine learning services and explains the usefulness of each.


Who will benefit from this course

  • Solutions Architects
  • Programmers 
  • Cloud Developers
  • Cloud Software Engineers
  • DevOps Professionals

Course Objectives 

After completing this Google Cloud Certification training, you should be able to:

  • Understand Google Cloud Platform concepts and terminologies
  • Understand GCP products and services
  • Learn methods to develop, implement, and deploy solutions
  • Distinguish between features of similar or related products and technologies
  • Learn to work with the Compute Engine, Cloud Storage, Cloud SQL and Big Query GCP services
  • Manage GCP environments using three different methods (Console, App, Command-Line)
  • Develop knowledge of solution patterns -- methods, technologies, and designs that are used to implement security, scalability, high availability, and other desired qualities
  • Understand basics of Big Data and Machine Learning services of GCP

Course Outline

Introducing Google Cloud Platform

Google Cloud Platform offers four main kinds of services: Compute, Storage, Big Data, and Machine Learning. This course focuses mostly on the first two, together with Google Virtual Private Cloud (VPC) networking. This module orients learners to the basics of Google Cloud Platform. It traces the evolution of cloud computing and explains what is unique about Google's approach to it. The module introduces the key structural concepts of regions and zones

Getting Started with Google Cloud Platform

GCP customers use projects to organize the resources they use. They use Google Cloud Identity and Access Management, also called “IAM,” to control who can do what with those resources. They use any of several technologies to connect with GCP. This module covers each of these topics, and it introduces a service called Cloud Launcher that is an easy way to get started with GCP.

Virtual Machines in the Cloud

Compute Engine lets you run virtual machines on Google’s global infrastructure. This module covers how Compute Engine works, with a focus on Google virtual networking. Every application needs to store data. Different applications and workloads require different storage and database solutions. This module describes and differentiates among GCP's core storage options: Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Datastore, and Google Bigtable. 

Containers in the Cloud

Containers are simple and interoperable, and they enable seamless, fine-grained scaling. Kubernetes is an orchestration layer for containers. Kubernetes Engine is Kubernetes as a service, a scalable managed offering that runs on Google’s infrastructure. You direct the creation of a cluster, and Kubernetes Engine schedules your containers into the cluster and manages them automatically, based on requirements you define. This module explains how Kubernetes Engine works and how it helps deploy applications in containers.

Applications in the Cloud

App Engine is a Platform-as-a-Service ("PaaS") offering. The App Engine platform manages the hardware and networking infrastructure required to run your code. App Engine provides built-in services that many web applications need. This module describes how App Engine works.

Developing, Deploying and Monitoring in the Cloud

Popular tools for development, deployment, and monitoring just work in GCP. Customers also have options for tools in each of these three areas that are tightly integrated with GCP. This module covers those tools.

Big Data and Machine Learning in the Cloud

GCP's big-data and machine learning offerings are intended to help customers get the most out of data. These tools are intended to be simple and practical to embed in your applications. This module describes the available big-data and machine learning services and explains the usefulness of each.

Who will benefit from this course

  • Solutions Architects
  • Programmers 
  • Cloud Developers
  • Cloud Software Engineers
  • DevOps Professionals

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