Developing Applications with Google Cloud Platform

Name: Developing Applications with Google Cloud Platform
Price: 1795.00 EUR

Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem on this three-day course.

3 day course

Supporting material

Virtual, Private

Virtual Classroom

A convenient and interactive learning experience, that enables you to attend on of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

As a trusted Google Cloud Partner, Jellyfish has been selected by Google to facilitate the delivery of this three day course.

Through a combination of presentations, demos, and hands-on labs, you’ll learn how to use Google Cloud Platform services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

Designed for application developers, on this course we’ll share best practices for application development and using Cloud Datastore and Google Cloud Storage.

This Developing Applications with Google Cloud Platform is offered as a private training session and can be delivered at our own training venue in the World Trade Center, Barcelona, any location of your choice or via Virtual Classroom.

Course overview

Who should attend:

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

Walk away with the ability 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 Kubernetes Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex

Prerequisites

To get the most benefit from this course you should have completed Google Cloud Platform Fundamentals or have equivalent experience.

It’s recommended that you have a working knowledge of Node.js. You will need a basic proficiency with command-line tools and Linux operating system environments.

Course agenda

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

Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK

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

Module 3: Overview of Data Storage Options

Overview of options to store application data
Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner

Module 4: Best Practices for Using Google Cloud Datastore

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

Module 5: Performing Operations on Buckets and Objects

Operations that can be performed on buckets and objects
Consistency model
Error handling

Module 6: Best Practices for Using Google Cloud Storage

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

Module 7: Handling Authentication and Authorization

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

Module 8: Using Google Cloud Pub/Sub to Integrate Components of Your Application

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

Module 9: Adding Intelligence to Your Application

Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API

Module 10: Using Google Cloud Functions for Event-Driven Processing

Key concepts such as triggers, background functions, HTTP functions
Use cases
Developing and deploying functions
Logging, error reporting, and monitoring

Module 11: Managing APIs with Google Cloud Endpoints

Open API deployment configuration
Lab: Deploy an API for your application

Module 12: Deploying an Application by Using Google Cloud Cloud Build, Google Cloud Container Registry, and Google Cloud Deployment Manager

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

Module 13: Execution Environments for Your Application

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

Module 14: Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver

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 optimise performance

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