Google Cloud Certified Professional Cloud Developer - Agilitics

Google Cloud Certified
Professional Cloud Developer

A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.

Overview
Course Outline
Target Audience
Overview

Duration: 4 days
Time: 9am to 5pm

What Will Be Taught For This Google Cloud Certifiied Professional Cloud Developer Course?

In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
Course Outline

The course includes presentations, demonstrations, and hands-on labs.

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
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
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 optimize performance

Target Audience

  1. Who Should Attend This Google Cloud Certified Developer Training?

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

Pre-requisites

To get the most of out of this course, participants should have:


Training was fantatstic
Our team Big data Agilitics Singapore
Kata San
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