GCP-DAGCP - Developing Applications with Google Cloud Platform

DURATION
3 days

COURSE DESCRIPTION
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.

OBJECTIVES
This course teaches participants the following skills:

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

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

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

Completed Google Cloud Platform Fundamentals or have equivalent experience
Working ​knowledge ​of Node.js
Basic proficiency with command-line tools and Linux operating system environments

TOPICS
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 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 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 ​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 ​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 optimize performance