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      Developing Applications With Google Cloud Platform, Toronto in Toronto


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      January 21, 2019

      Monday   9:00 AM

      Queen Street West
      Toronto, Ontario

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      Developing Applications With Google Cloud Platform, Toronto

      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 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 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

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