Become familiar with the processes of designing, developing, and deploying apps that can integrate Google Cloud ecosystem components without a hitch. This course will teach you how to utilize Google Cloud services and pretrained machine learning APIs to build cloud-native applications that are secure, scalable, and intelligent. You will master these skills through a combination of lectures, demonstrations, and hands-on exercises.
Objectives
Apply industry standards when developing software applications.
Determine the most suitable method of data storage for the application’s files.
Implement federated identity management
Create application components or microservices that have a loose coupling between them.
Integrate application components and data sources
Debuggers and tracers are used for application monitoring.
Use containers and deployment services to carry out deployments in a manner that is repeatable.
Make sure you pick the right environment for running the program.
Audience
Developers of apps who are interested in creating cloud-native applications or redesigning existing applications so that they can operate on Google Cloud.
Prerequisites
Participants in this class should have the following in order to get the most out of it:
Google Cloud Fundamentals successfully completed: Core Infrastructure or have similar expertise
Practical experience with Node.js, Python, or Java Proficient familiarity with command-line tools and environments native to Linux
Course Outline
The first module will cover application development best practices.
Code and Environment Management
The design and development of microservices and application components that are secure, scalable, and reliable despite their loose coupling.
Integration and Delivery on a Continuous Basis
Applications Re-Architectured for Hosting in the Cloud
The second module is titled “Getting Started with Google Cloud Development.”
An overview of the services offered by Google Cloud for applications and scripts:
Google Cloud APIs
Cloud SDK Cloud Client Libraries
Demonstration of the Cloud Shell Cloud Code: In this section of the Google APIs Explorer Lab, we will discuss configuring a development environment.
Module 3: An Analysis of the Available Data Storage Options
A Comprehensive Look at the Available Options for the Storage of Application Data Use Cases for Cloud Storage, Firestore, Cloud Bigtable, Cloud SQL, and Cloud Spanner
Creating a Secure Connection to a Cloud-Based SQL Database (Demo)
The Fourth Module Will Teach You the Most Effective Ways to Use Datastore
Using Firestore in Datastore Mode: Recommendations and Best Practices for the Following
Indexes, both Built-In and Composite, Are Queried
Inserting and Deleting Data (Batch Operations)
Transactions
Error Handling
Demo: Explore Datastore
Demo: Utilize Dataflow in Order to Perform Bulk Data Loading into Datastore Lab: Keeping Track of Application Information in Datastores
The fifth module will teach you how to perform operations on buckets and objects.
Explore Cloud Storage Request Endpoints, Composite Objects, and Parallel Uploads with This Demonstration of the Cloud Storage Concepts Consistency Model
Enable CORS Configuration in Cloud Storage as a Part of the Truncated Exponential Backoff Demo
Best Practices for Utilizing Cloud Storage is the Topic of Module 6
Assigning Names to Buckets for Use on Static Websites and Other Applications
Naming Objects (From an Access Distribution Perspective)
Lab on Performance Considerations: Storing Photographs and Videos in the Cloud Storage
The Management of Authentication and Authorization is Covered in Module 7.
Roles and Service Accounts for Identity and Access Management (IAM) Systems
Authentication of Users Through the Use of Firebase Authentication
Authentication and Authorization of Users Made Possible Through the Use of Identity-Aware Proxy Lab: Authentication of Users within Your Application
Utilizing Pub/Sub to Integrate Components of Your Application is the Topic of Module 8
Topics, Publishers, and Subscribers all come into play here.
Subscriptions that are Pulled and Pushed
Developing a Backend Service Use Cases for the Pub/Sub Lab
Bringing Intelligence to Your Application is the Topic of Module 9
An introduction to pre-trained machine learning application programming interfaces (APIs), including the Vision API and the Cloud Natural Language Processing API
The use of Cloud Functions for Event-Driven Processing is covered in Module 10.
Concepts Crucial to the Whole Process, Such as Triggers, Background Functions, and HTTP Functions
Use Cases
Functions Related to the Development and Implementation of
Monitoring, as well as Logging, and Reporting Errors
Demonstration of Calling Cloud Functions Using a Straightforward Request-response Model
Processing Pub/Sub Data Utilizing Cloud Functions is the Aim of This Lab.
The eleventh module will teach you how to manage APIs using cloud endpoints.
Configuration Lab for Open API Deployment: Implementing an API for the Quiz Application
Module 12 will cover the process of deploying applications.
Repeatable Deployments through the Use of Deployment Configuration and Templates Container Image Creation and Storage
Cloud Build and Cloud Container Registry: An Investigation of the Cloud Demo: Putting the Application into the Kubernetes Engine Deployment
Module 13: Calculate the Available Choices for Your Application
The following are some things to keep in mind when selecting a compute option for your application or service:
Computer Processing Unit
Google Kubernetes Engine (GKE)
Cloud Run
Cloud Functions
Comparisons of Different Platforms
Putting App Engine and Cloud Run Side-by-Side
Performance Debugging, Monitoring, and Tuning is the Topic of Module 14
Google Cloud’s operations suite
Lab for Managing Performance and Analyzing Application Faults and Bugs
