Deployment of a Web Application to automate talent onboarding using HTML / CSS / JS / BS (Front-end) + Python Flask (Back-end) with Cloud Storage and Cloud Functions 100% Serverless — Part 2
In this project based on a real-world scenario, I acted as a Cloud Expert in the deployment of a Web Application to automate the HR onboarding process. By filling out a form, the user is created in the Training Portal in a fully automated way.
To deploy this solution, I used a Moodle image available on GCP Marketplace, where the REST API had been enabled, running on Compute Engine, front-end on Cloud Storage, and the back-end on Cloud Run.
I deployed the back-end using Python Flask using Cloud Functions, 100% Serverless. Then, I deployed the front-end using HTML/CSS/JS/BS on Cloud Storage to store the static files of the Web Application. See below the solution architecture for this project:
For the first part of this project, was enabled the API in Moodle.
In the following, I validated if the API it’s working.
For the test, it’s necessary to have the information of the Moodle IP and Token generated of the previous to obtain the result.
I loaded the project files inside Google Cloud, these files have the Backend and Frontend files that were used in the deployment.
Already with the unzip files, I used the Cloud Functions resource of Google and loaded the codes inside the Main and Requirements files to then provision the Backend.
With the deploy finished, I accessed the moodleCriaUsuario function and did a copy the trigger URL to be used in the next step.
In the Cloud Shell, I verified if the function is functioning correctly, inserting new input data.
At this point, I started the Frontend deployment.
In the frontend files, I accessed the index.html file and edited the action parameter, replacing the previous URL with the Trigger URL created by the Cloud Function.
With the Cloud Shell, I created a bucket in the Cloud Storage of Google.
I uploaded all the files of the Frontend to the bucket.
Finally, I configured the bucket, where was set the index.html file to the homepage and the 404.html file in case of failure. Also, I changed the permission so that everyone can view all objects in the bucket.
To make the web page test, I used the authenticated URL of the Frontend, then I inserted the user input data on Onboarding page, these were stored in Google Storage and available on the iLearn page.
As a conclusion, this project exposed me to a real Cloud scenario where I implemented in a practical way a scenario that will help me the implement a web application using Serverless, Backend, Frontend resources for automation of talents onboarding where the talent’s analyst can perform the creation of user in the Training Portal of form totally automated.
