deploy-cloud-functions
serverless-application-model
deploy-cloud-functions | serverless-application-model | |
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18 | 98 | |
290 | 9,237 | |
2.1% | 0.2% | |
6.0 | 9.2 | |
about 1 month ago | 9 days ago | |
TypeScript | Python | |
Apache License 2.0 | Apache License 2.0 |
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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.
serverless-application-model
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Simple and Cost-Effective Testing Using Functions
The complete solution with SAM is available here.
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Deploying a Serverless Dash App with AWS SAM and Lambda
There are many options to deploy Serverless Applications in AWS and one of them is SAM, the Serverless Application Model. I chose to use it here, because it doesn't add too many layers of abstraction between what's being deployed and the code we write and our infrastructure is quite simple.
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Serverless Apache Zeppelin on AWS
The solution uses AWS SAM with the global configuration for Lambda functions and the public API you can use to access Apache Zeppelin. The stack deployment provides the URL as an output value.
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Using design patterns in AWS Lambda
When you combine this with the AWS Serverless Application Model you can also very easily include your dependencies. Or use a compiled language like golang for your Lambda functions. You simply run sam build before you run the aws cloudformation package and aws cloudformation deploy commands. SAM will build the binary and update the template to point to the newly built binary. Package will then upload it to S3 and replace the local reference to the S3 location. Deploy can then create or update the stack or you can use the CloudFormation integration in CodePipeline.
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Serverless Site Health Check Notification System
I'm a big fan of using an Infrastructure as Code (IaC) approach for any project. My go to tools for this are the Servlerless Application Model (SAM) and it's associated CLI (SAM CLI). For more official use cases and for cross platform apps I typically use Terraform.
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Starting My AWS Certification Journey as a Certified Cloud Practitioner
AWS SAM
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API Gateway, Lambda, DynamoDB and Rust
Kicking off the tour and not starting a war, but I'm going to be using the Serverless Application Model.
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Consuming an SQS Event with Lambda and Rust
The diagram here is super simple. I'm going to write something a little later that shows how this code could fit into a bigger workflow, but for now, I'm keeping it basic. And yes, that's the SAM Squirrel in there.
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AWS Data Engineer Associate Certification - Coming Soon
Interestingly, AWS CDK and SAM are both explicitly mentioned. While CDK broadly addresses Infrastructure as Code, SAM is highlighted for its role in developing serverless data pipelines - a hugely underrated concept.
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A Beginner's Guide to the Serverless Application Model (SAM)
Naturally, there are several options available to declare your cloud resources. The options with the most popularity are the CDK, AWS CloudFormation, SST, Serverless framework, Terraform, and AWS SAM. There are others, but when talking about Infrastructure as Code (IaC), these are the ones you hear about most often.
What are some alternatives?
strapi-connector-firestore - Strapi database connector for Firestore database on Google Cloud Platform.
aws-elastic-beanstalk-cli - The EB CLI is a command line interface for Elastic Beanstalk that provides interactive commands that simplify creating, updating and monitoring environments from a local repository.
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.
LocalStack - 💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline
dockerfile-rails - Provides a Rails generator to produce Dockerfiles and related files.
Moto - A library that allows you to easily mock out tests based on AWS infrastructure.
functions-samples - Collection of sample apps showcasing popular use cases using Cloud Functions for Firebase
sst-start-demo - A simple SST app to demo the new `sst start` command
go - The Go programming language
openvscode-server - Run upstream VS Code on a remote machine with access through a modern web browser from any device, anywhere.
django-simple-deploy - A reusable Django app that configures your project for deployment
aws-sam-cli - CLI tool to build, test, debug, and deploy Serverless applications using AWS SAM