Tuesday, 21. May 2019, Mountain View, Developing Applications With Google Cloud Platform, Mountain View

from 21. May 2019 - 9:00
till 23. May 2019 - 18:00

Mountain View

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Event description
Developing Applications with Google Cloud Platform
(Formerly CPD200)
(3 days)
 
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. A personal laptop is required for all workshops and will not be provided.
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

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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


Course Outline
 
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: Securing ​Your Application

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 *****

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: : ​Using ​Cloud ​Endpoints to ​Deploy ​APIs

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

Module 12: Debugging ​Your Application ​by ​Using ​Google Stackdriver

Stackdriver ​Debugger
Stackdriver ​Error ​Reporting
Lab: ​Debugging ​an ​application ​error ​by ​using ​Stackdriver ​Debugger and ​Error ​Reporting

Module 13: Deploying ​an Application ​by ​Using ​Google ​Cloud Container ​Builder, ​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 ​Flex ​test ​and ​production ​environments

Module 14: Execution Environments ​for ​Your ​Application

Considerations ​for ​choosing ​an ​execution ​environment ​for ​your application ​or ​service:

Google ​Compute ​Engine
Container ​Engine
App ​Engine ​Flex
Cloud ​Functions
Cloud ​Dataflow


Lab: ​Deploying ​your ​application ​on ​App ​Engine ​Flex

Module 15: ​Monitoring ​and ​Tuning Performance

Best ​practices ​and ​watchpoints ​for ​performance
Key ​concepts ​related ​to ​Stackdriver ​Trace ​and ​Stackdriver ​Monitoring
Detecting ​and ​resolving ​performance ​issues
Lab: ​Use ​Stackdriver ​Monitoring ​and ​Stackdriver ​Trace ​to ​trace ​a request ​across ​services, ​observe, ​and ​optimize ​performance

Developing Applications With Google Cloud Platform, Mountain View, Mountain View event

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