Developing Applications with Google Cloud Platform (DAGCP)

Course Details

Online Training

Duration : 3 day

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.

Who should attend

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


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 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

Course Content

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