Google Cloud Function with 5 Hands-on Demo - Serverless GCP

Explore Google Cloud functions, a serverless function-as-a-service for single-purpose tasks. Learn event-driven triggers, auto scaling, multiple runtimes, and pay-as-you-go pricing with isolated executions.

Create a Google Cloud free trial account by signing in with an existing Google account and providing billing details. Activate $300 credit for three months to explore cloud products.

Create a Google Cloud project to house and provision all resources within it. Understand project name, ID, and number, the 20-project quota, and attaching a billing account to enable deployment.

Deploy your first Google Cloud function to Google Cloud, selecting a region and an http trigger with unauthenticated access, then verify the hello world output.

Deploy a google cloud function with an http based trigger, using default parameters and python code, then trigger it via a GitHub webhook to send messages to Slack.

Deploy a Google Cloud Function with an http trigger, using default parameters and unauthenticated access, set runtime Python 3.7, deploy and test, and prepare for GitHub webhook in next video.

Set up a GitHub webhook to trigger a Google Cloud Function from push events, deploy the function, and verify via logs before integrating with Slack.

Set up a Slack workspace, create a Slack app, enable an incoming webhook, and deploy a Google Cloud Function that posts a GitHub code-commit message to a channel.

Test a Google Cloud function with a GitHub webhook to Slack, verify logs and a 200 response, and learn through this hands-on case study.

Explore case study 2 by wiring a Firestore trigger to a Google Cloud Function and publishing changes to a Pub/Sub topic, using a student collection with name and age.

Deploy a second Google Cloud Function with a Firestore trigger and create a Firestore database with a student collection to handle document changes.

Create a pubsub topic and subscription, choose pull delivery, adjust message retention and expiration, and publish test messages from the cloud console as a prelude to cloud function integration.

Publish cloud Firestore events to cloud pub sub using a python cloud function, with requirements.txt, a publisher client, and json messages to a topic path.

Test function two demonstrates triggering a cloud function from Firestore to publish a json event to Cloud Pub/Sub, validating the end-to-end flow from document creation to subscription delivery.

Case study three introduces spatial processing in a Google Cloud function using the Vision API to detect faces and output coordinates to an out bucket from Cloud Storage uploads.

Create Google Cloud storage buckets for input and output, with unique names in us central1 (Iowa) and standard storage class. Upload a face photo to illustrate; no trigger yet.

Deploy a Google Cloud function named detect_face with a cloud storage trigger on a specified bucket, handling file uploads and logging file name and bucket details.

Build and deploy a Google Cloud function that uses the Cloud Vision API to detect faces in uploaded images, configure Vision and storage clients, and test the workflow end-to-end.

Learn how to upload face data responses from Google Cloud Vision API to Google Cloud Storage using a cloud function, with a temporary file, deployment, and end-to-end testing.

Perform end-to-end testing of a Google Cloud Function by uploading photos to Google Cloud Storage, using Vision API for face detection, and validating x and y coordinates in responses.

Trigger a google cloud function with pub/sub as the source to integrate with the twilio api and send sms, by creating a pub/sub topic, deploying the function, and configuring twilio.

Create a new Google Cloud Pub/Sub topic and deploy a Python 3.7 cloud function triggered by Pub/Sub; decode base64 data and log messages for future Twilio delivery.

Learn to set up a Twilio account and send SMS via Python Twilio API in a Google Cloud Function, using a trial number and credentials, with Pub/Sub integration and deployment.

Execute an end-to-end test of a Google Cloud Function triggered by Pub/Sub, publish a message, inspect logs, and verify SMS delivery via Twilio with a distinct message id.

Case study five introduces using a firebase real-time database as a trigger for a google cloud function, with options to dump data to BigQuery, Bigtable, Spanner, or Cloud SQL.

Deploy a google cloud function triggered by firebase real-time database events like create, update, or delete. Set a hierarchical path and debug firebase configuration before redeploy.

Set up the Firebase real time database in the Firebase console for the GCF course, configure US Central and lock mode, and verify deployment.

Test a google cloud function triggered by firebase database changes, validate triggers while building hello foo and bar hierarchy; explore sending data to bigtable or bigquery with python sdk.

Explore Google Cloud function pricing, including free quotas, invocation costs, memory and CPU compute time, data transfer, and how to use the pricing calculator for second-generation functions.

Set and access runtime and build environment variables in a Google Cloud Function using Python and the os module, and test deployments to verify variables like foo and test.

Understand default and maximum timeouts for first and second generation Google Cloud Functions, and see a hands-on demo of timeout enforcement and termination.

Learn traffic splitting in Google Cloud Functions and Cloud Run, deploying two revisions (version one and version two), and progressively routing 50/50 traffic before full rollout.

Explore how second generation Google Cloud Function increases runtime to 60 minutes for http triggers, boosts memory and concurrency, enables traffic splitting, Cloud Run integration, Eventarc support, and artifact registry.