Developing Applications with Google Cloud Platform

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.

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.

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.

Best Practices for Using Google 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.

Performing Operations on Buckets and Objects

• Operations that can be performed on buckets and objects.
• Consistency model.
• Error handling.

Best Practices for Using Google 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.

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.

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.

Adding Intelligence to Your Application

• Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API.

Using Google 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.

Managing APIs with Google Cloud Endpoints

• Open API deployment configuration.
• Lab: Deploy an API for your application.

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 flexible environment test and production environments.

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.

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.