Developing Applications with Google Cloud Specialization
In this specialization, application developers learn how to design, develop, and deploy applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Learners can choose to complete labs in their favorite language: Node.js, Java, or Python.
This class is intended for application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud.
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 Kubernetes Engine as a runtime environment and later switch to a no-ops solution with a Google App Engine flexible environment.
Apr 4, 2020
This course was really nice. It helped me to learn the basics of docker and Kubernetes. Also, learned how to debug apps in GCP. It’s really nice learning experience. Thanks a lot!
By Robert F
By CIRIELLO V
Mar 19, 2022
Fantastic Specialization that explains the foundation of Application Development on the Cloud and with many Labs for Java Spring, Node JS, and Python.
The specialization explains the best practices of cloud application development that, thanks to microservices architecture and cloud technologies allow to obtain the three fundamental features of a modern application:
-Scalability and High Availability
The specialization explains in detail how this goal can be reached thanks to a design of the application with loosely coupled components, asynchronous communication, and the stateless paradigm for scalability.
The specialization explains the many services Cloud Technology offers and how the Designer should correctly choose among these solutions according to the Business, User Case, and Pay Model needs.
– IAM: Identity and Access Management for security (user authentication and authorization )
– Data Storage Services according to the Application Needs (Cloud Storage, Datastore, Cloud BigTable, CloudSQL, Cloud Spanner, and BigQuery for BigData)
-Artificial Intelligence services: Vision, Speech, Translation, Natural Language, Video Intelligence
-Pub-Sub services for asynchronous and highly scalable communications
-Application Environment services: Compute Engine for the classic VM, App Engine for applications released as a container, Cloud Run for containerized applications, Cloud Functions for API Gateway Architecture. The services allow the creation of very rapid way load balancing and horizontal scalability for high-traffic and low-latency responses.
The specialization explains all the APIs the Cloud makes available to users and developers for all these services. I build a Python script that realizes the Face Detection on images thanks to the use of the Cloud Vision API in the very short time since the Cloud contains the AI pre-trained model ready to use thanks also Rest calls. The result of my script with the Vision API was amazing: it analyzed the images for face detection in a fantastic way.
The Specialization explains all the steps and the code needed to integrate applications with the Cloud technology. Describe how to monitor your application in Production with Logging and Metrics and the possibility to easily use the Site Reliability Engineering (SRE) to define and monitor Service Level Indication (SLI) as :
– latency SLI (ratio of the number of calls below a latency threshold to the number of all calls)
– availability SLI is the ratio of the number of successful responses to the number of all responses
It also described the Cloud Source Repository for Debugging and Versioning. In the Lab, I created an A/B Testing very easily thanks to the Versioning feature offered by the Cloud.
By senn l
Sep 13, 2019
Overall this specialization is learning to deliver applications on the GCP cloud. I have noticed that many costs are hidden, and the cost factors are unknown until after the launch. Enterprise cloud users (management) are usually shocked because they only see the bill after the product launch; it would be too late for them to change anything (I learned that from using AWS). To be helpful and fair to enterprise users, Cloud vendor(s) should integrate cost threshold configuration settings to supporting features along with providing GCP system engineer to work with the clients on the initial deliverable.
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