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Developing Applications with Google Cloud Platform (DAGCP)

Course Description Schedule
 

Course Overview

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.

Profil słuchaczy

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

Wymagania

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

Cel szkolenia

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

Program szkolenia

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 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: ​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
Classroom training
Modality: C

Trwa 3 dni

Cena (excl. tax)
  • Polska: 6.300,- PLN
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2019-04-16 - 2019-04-18 Berlin Enroll
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This is an Instructor-Led Online course
This is a FLEX course, which is delivered both virtually and in the classroom.
 

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