Developing Applications with Google Cloud Platform Training

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Developing Applications with Google Cloud Platform Course Overview

Enroll for 3-day Developing Applications with Google Cloud platform certification training course from Koenig Solutions accredited by Google. Developing Application with Google Cloud Platform certification course developers will learn how to design, develop and deploy applications that seamlessly integrate components from the Google Cloud ecosystem.

Through a blend of hands-on labs and interactive lectures, participants will learn how to use GCP services and pre-trained machine learning API’s to build secure, scalable and intelligent cloud-native applications.

 

Target Audience:

Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform.

 

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

The 1-on-1 Advantage
Methodology
Flexible Dates
  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option
4-Hour Sessions

You will learn:

Module 1: Best Practices for Application Development
  • Code and environment management
  • Design and development of secure, scalable, reliable, loosely coupled application components and microservices
  • Continuous integration and delivery
  • Re-architecting applications for the cloud
  • How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
  • Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials
  • Overview of options to store application data
  • Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner
  • Best practices related to the following:
  • Queries
  • Built-in and composite indexes
  • Inserting and deleting data (batch operations)
  • Transactions
  • Error handling
  • Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow
  • Lab: Store application data in Cloud Datastore
  • Operations that can be performed on buckets and objects
  • Consistency model
  • Error handling
  • Naming buckets for static websites and other uses
  • Naming objects (from an access distribution perspective)
  • Performance considerations
  • Setting up and debugging a CORS configuration on a bucket
  • Lab: Store files in Cloud Storage
  • Cloud Identity and Access Management (IAM) roles and service accounts
  • User authentication by using Firebase Authentication
  • User authentication and authorization by using Cloud Identity-Aware Proxy
  • Lab: Authenticate users by using Firebase Authentication
  • Topics, publishers, and subscribers
  • Pull and push subscriptions
  • Use cases for Cloud Pub/Sub
  • Lab: Develop a backend service to process messages in a message queue
  • Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API
  • Key concepts such as triggers, background functions, HTTP functions
  • Use cases
  • Developing and deploying functions
  • Logging, error reporting, and monitoring
  • Open API deployment configuration
  • Lab: Deploy an API for your application
  • Creating and storing container images
  • Repeatable deployments with deployment configuration and templates
  • Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments
  • Considerations for choosing an execution environment for your application or service:
  • Google Compute Engine
  • Kubernetes Engine
  • App Engine flexible environment
  • Cloud Functions
  • Cloud Dataflow
  • Lab: Deploying your application on App Engine flexible environment
  • Stackdriver Debugger
  • Stackdriver Error Reporting
  • Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting
  • Stackdriver Logging
  • Key concepts related to Stackdriver Trace and Stackdriver Monitoring
  • Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance
Live Online Training (Duration : 24 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites
  • Completed GCP Fundamentals or have equivalent experience.
  • Working knowledge of Node.js.
  • Basic Proficiency with command-line tools and Linux operating system environments.

After completing this course, you will be able to:

 

  • Use best practices for application development.
  • Choose the appropriate data storage option for application data.
  • Implement federated identity management.
  • Develop loosely coupled application components or microservices.
  • Integrate application components and data sources.
  • Debug, trace, and monitor applications.
  • Perform repeatable deployments with containers and deployment services.
  • Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex.

 

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FAQ's


Yes, fee excludes local taxes.
Yes, we do.
The Fee includes:
  • Courseware
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1-on-1 you can select your own schedule, other students can be merged but you select the schedule. Choose 1-on-1 if published schedule do not meet your requirement. If you also want a private session, opt for 1-on-1 Public.
Yes.
No, it is not included.

We offer below courses:

In both, you choose the schedule. In public, other participants can join, Private other participants want to join.
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