GOOGLE CLOUD
Developing Applications with Google Cloud
Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pretrained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
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
Intermediate
3 x 8 hour sessions
Delivered in English
Completed Google Cloud Fundamentals: Core Infrastructure or have equivalent experience
Working knowledge of Node.js, Python, or Java
Basic proficiency with command-line tools and Linux operating system environments
Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud
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
Design and develop secure, scalable, reliable, loosely coupled application components and microservices.
Understand how to rearchitect applications for the cloud
Google Cloud APIs
Cloud SDK
Cloud Client Libraries
Cloud Shell
Cloud Code
Overview of options to store application data
Use cases for Cloud Storage, Firestore, Cloud Bigtable, Cloud SQL, and Cloud Spanner
Queries
Built-in and composite indexes
Inserting and deleting data (batch operations)
Transactions
Error handling
Cloud Storage concepts
Consistency model
Request endpoints
Composite objects and parallel uploads
Truncated exponential backoff
Naming buckets for static websites and other uses
Naming objects (from an access distribution perspective)
Performance considerations
Identity and Access Management (IAM) roles and service accounts
User authentication by using Firebase Authentication
User authentication and authorization by using Identity-Aware Proxy
Lab: Adding User Authentication to your Application
Topics, publishers, and subscribers
Pull and push subscriptions
Use cases for Pub/Sub
Overview of pre-trained machine learning APIs such as the Vision API and the Cloud Natural Language Processing API.
Key concepts such as triggers, background functions, HTTP functions
Use cases
Developing and deploying functions
Logging, error reporting, and monitoring
Open API deployment configuration
Creating and storing container images
Repeatable deployments with deployment configuration and templates
Considerations for choosing a compute option for your application or service:
• Compute Engine
• Google Kubernetes Engine (GKE)
• Cloud Run
• Cloud Functions
Platform comparisons.
• Comparing App Engine and Cloud Run
Google Cloud’s operations suite
Managing performance
Logging
Monitoring and tuning performance
Identifying and troubleshooting performance issues
Ref: T-GCPDEV-I-01
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