Turbine VS Google Cloud Functions

Turbine mentions (0)

Google Cloud Functions mentions (41)

• 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. - Source: dev.to / 5 months ago

• 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. Source: 6 months ago

• 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. - Source: dev.to / 9 months ago

• 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. Source: 11 months ago

• 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. Source: 11 months ago