deploy-cloud-functions
berglas
deploy-cloud-functions | berglas | |
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18 | 37 | |
290 | 1,224 | |
2.1% | 0.1% | |
6.0 | 6.9 | |
about 1 month ago | 4 days ago | |
TypeScript | Go | |
Apache License 2.0 | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deploy-cloud-functions
- Czym jest funkcja bezserwerowa?
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Increasing Your Cloud Function Development Velocity Using Dynamically Loading Python Classes
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.
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Need some advice on API key storage
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.
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Golden Ticket To Explore Google Cloud
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.
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Isolate a resource intensive task (in C++) from a Django Web app and restructure a web app
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
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Need Guidance
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.
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Read only API on Historical Data
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.
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Job Scheduling on Google Cloud Platform
Cloud Functions: A serverless platform for event-driven functions
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Moving my Android app to Google cloud
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.
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Serverless Compute
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.
berglas
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How to deploy a Django app to Google Cloud Run using Terraform
Secret Manager: secure storage for sensitive data e.g passwords.
- How do you handle sensitive variables with a service-worker?
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Increasing Your Cloud Function Development Velocity Using Dynamically Loading Python Classes
Google Secret Manager
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Getting started using Google APIs: API Keys (Part 2)
API keys are easy to "leak" or compromise, so best to not only use the restrictions presented to you when you create them but physically protect them as well. Don't code them in plain-text, don't check them into GitHub, etc. Store them in a secure database or use a service like GCP Secret Manager.
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Need some advice on API key storage
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.
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Secure GitHub Actions by pull_request_target
In this post, I described how to build secure GitHub Actions workflows by pull_request_target event instead of pull_request event. Using pull_request_target, you can prevent malicious codes from being executed in CI. And by managing secrets in secrets management services such as AWS Secrets Manager and Google Secret Manager and access them via OIDC, you can restrict the access to secrets securely. To migrate pull_request to pull_request_target, several modifications are needed. And pull_request_target has a drawback that it's difficult to test changes of workflows, so it's good to introduce pull_request_target to repositories that require strong permissions in CI. For example, a Terraform Monorepo tends to require strong permissions for CI, so it's good to introduce pull_request_target to it.
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Need Help with Deploying Directus on Google Cloud Platform (GCP)
If you want to make these secrets more secure and get versioning and access logs for them, you may want to switch to Secret Manager later on. They can still be exposed as environment variables to your code. It's a little more setup work, so start with the simple approach at the top.
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Has anyone been able to implement the OpenAI API with a Firebase Function (which is needed for the env variable API Key)?
https://cloud.google.com/secret-manager https://aws.amazon.com/secrets-manager/
- Securely storing Social Security Numbers with Firebase?
- Dónde van las credenciales cuando voy a subir un código a la nube para correr 24/7?
What are some alternatives?
strapi-connector-firestore - Strapi database connector for Firestore database on Google Cloud Platform.
kubernetes-external-secrets - Integrate external secret management systems with Kubernetes
90DaysOfDevOps - This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
helm-charts
dockerfile-rails - Provides a Rails generator to produce Dockerfiles and related files.
kube-secrets-init - Kubernetes mutating webhook for `secrets-init` injection
functions-samples - Collection of sample apps showcasing popular use cases using Cloud Functions for Firebase
gitleaks - Protect and discover secrets using Gitleaks 🔑
go - The Go programming language
cocert - Split and distribute your private keys securely amongst untrusted network
django-simple-deploy - A reusable Django app that configures your project for deployment
secrets-store-csi-driver-provider-gcp - Google Secret Manager provider for the Secret Store CSI Driver.