90DaysOfDevOps VS deploy-cloud-functions

90DaysOfDevOps 2022

I am trying to get into DevOps following https://github.com/MichaelCade/90DaysOfDevOps/tree/main

Sorry I don't have any particular project atm. However, you can take a look at this: https://github.com/MichaelCade/90DaysOfDevOps/blob/main/2022.md

I've been contributing to an Online challenge called #90DaysOfDevOps for its second "season". This was started in 2022 by a friend who was changing roles and wanted to accelerate his learning journey. I wrote a section on Red Hat OpenShift, as I went through learning more about how it works and operates.

90DaysOfDevOps

*This is part of a series that will be covered here, but I also encourage you to follow along with the rest of the series on 90DaysOfDevOps.

*As a quick disclaimer - as I am an AWS Serverless Hero, most of the examples and explanations I give will reference the AWS ecosystem since that is where my expertise is. Many of the AWS services and tools we will discuss have equivalents across Azure, GCP, or other tooling. I will do my best to call these out going forward. This is part of a series that will be covered here, as well as part of a larger effort on 90DaysOfDevOps, which I highly encourage you to follow along with.

One of the issues developers can encounter when developing in Cloud Functions is the time taken to deploy changes. You can help reduce this time by dynamically loading some of your Python classes. This allows you to make iterative changes to just the area of your application that you’re working on.

I've been looking at Google Secret Manager which sounds promising but I've not been able to find any examples or tutorials that help with the actual practical details of best practice or getting this working. I'm currently reading about Cloud Functions which also sound promising but again, I'm just going deeper and deeper into GCP without feeling like I'm gaining any useful insights.

Serverless computing was also introduced, where the developers focus on their code instead of server configuration.Google offers serverless technologies that include Cloud Functions and Cloud Run.Cloud Functions manages event-driven code and offers a pay-as-you-go service, while Cloud Run allows clients to deploy their containerized microservice applications in a managed environment.

Lambda is made for your use case :). It doesn’t have to be AWS there are plenty of other serverless computing services like: - Google cloud functions - Azure functions Etc

Once you have some basic familiarity with programming, try deploying one of your Python programs to the cloud. Start with Cloud Functions, because that doesn't require any knowledge of Linux server administration.

If the customer prefers making REST-like calls: Deploy a simple Cloud Function that the customer would invoke by making a regular HTTP call with some parameters. The Cloud Function would validate the customer's credentials, and then send a query to BigQuery using one of the client libraries. You can write Cloud Functions in Node.js, Python, Go, Java, C#, Ruby, or PHP. You are only charged when the function runs.

Cloud Functions: A serverless platform for event-driven functions

I propose starting with Cloud Functions. You can use your Python experience, you can do rapid prototyping by writing your code in a text-box in the Google Cloud Console, and there will be no server setup or maintenance.

AWS Lambda If you're in Azure, your equivalent service is Azure Functions. For Google, this is Google Functions (yes, AWS just HAD to be different). Regardless of its name, all of these services fulfill the same purpose - a small compute building block to house your business logic code. An AWS Lambda function is simply the code you want to run, written in your language of choice (I preference Python, but Typescript and Java are popular options). In your infrastructure code, you specify some lambda function basics, like name, path to the business logic code, security role, and what runtime you're using, and optionally have the ability to control more parameters like timeout, concurrency, aliases, and more. Lambda even has built in integrations to other AWS services, such as S3 and SQS (we'll get to these) to make application development even easier. Additionally, lambda functions are priced based on the number of times they're invoked and the duration of time they run, making them exceptionally affordable.