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In April 2008, Google announced App Engine, a platform for developing and hosting web applications in Google-managed data centers, which was the first cloud computing service from the company. The service became generally available in November 2011. Since the announcement of App Engine, Google ...

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Google Cloud | 2,270,230 followers on LinkedIn. Welcome to the new way to cloud. | Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that ...

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Information on valuation, funding, cap tables, investors, and executives for Google Cloud Platform. Use the PitchBook Platform to explore the full profile.

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Google Cloud Next ’24. Explore keynotes, live demos, and content from around the world from visionaries, thought leaders, developers, and Google experts.

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Google Cloud focuses on business challenges that head-on with cloud computing services from Google.

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Learn about Google Cloud and its core public cloud services, including Google Compute Engine, Google App Engine and Google Cloud Storage.

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As a Google Cloud development company, we offer a wide array of services, including Google Cloud app development and modernization, cloud migration and infrastructure management, and data management and analytics.

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On the other hand, Google Cloud Platform is mostly used in the industry to describe the suite of cloud computing services available for end users and companies who want to build their own digital cloud infrastructures. It is interesting to note that products like YouTube, Gmail and the generative ...

Google Cloud Platform: What Is It, and Should You Use It?

Check out this list of the top Google Cloud companies. See company benefits, info, interviews and more at Built In.

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Use the power of Google to search across your company’s content. From Gmail and Drive to Docs, Sheets, Slides, Calendar, and more, Google Cloud Search answers your questions and delivers relevant suggestions to help you throughout the day.

Google Cloud Search: Smart Business Database | Google Workspace

There was a significant gap between ... the company relied on data provided by third-party sales partners and distributors · Ferrero wanted to create a digital ecosystem where there was a direct point of contact with its customers and also a foundation for an innovative, data-driven marketing strategy · BigQuery was the answer to Ferrero&#x27;s challenges since it was capable of super-fast and efficient data analysis · Using Google Cloud Platform’s ...

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Here’s a quick overview of some key differentiators between the leading cloud platform providers: Google Cloud Platform vs. Microsoft Azure vs. Amazon Web Services. ... A Swiss company founded in Singapore in 2003, Acronis has 15 offices worldwide and employees in 50+ countries.

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Learn why many of the world’s leading companies are choosing Google Cloud to solve their toughest challenges.

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You’ll notice some big names up there in the top ten monthly spenders. Now it’s time to see just how these companies are using Google Cloud Platform within their organisation via some quick case studies. Use them to see if any of their use cases might be some inspiration in how GCP could ...

Who's Using Google Cloud Platform? | Contino | Global Transformation Consultancy

Answer (1 of 5): Some notable companies using Google Cloud Platform (Compute Engine or App Engine) use Spotify, HSBC, Home Depot, Snapchat, HTC, Best Buy, Philips, Coca Cola, Domino&#x27;s, Feedly, ShareThis, Sony Music, and Ubisoft. As a result, Google does not use or depend on any of the AWS servic...

Who are the biggest customers of Google Cloud Platform? - Quora

1,285,987 companies use Google Cloud Platform(GCP). Google Cloud Platform(GCP) is most often used by companies with 1-10 employees &amp; $1M-10M in revenue. Our usage data goes back 5 years and 7 months.

Companies using Google Cloud Platform(GCP) and its marketshare

Hi Brian, Thanks for the post, I was looking for a cloud hosting for my Magento store, No doubt, In fact, all the written points are concerned with the person who is looking for cloud hosting, selecting Google for hosting is not a bad choice over other hosting companies like Kyup or Vultr.

Top 10 Advantages of Choosing Google Cloud Hosting

With Amazon Microsoft ibm Oracle and more competing in the cloud wars, which big tech companies are using Google cloud? Facebook also uses their own…

r/googlecloud on Reddit: Biggest tech companies that use Google cloud?

 If you’re looking for a new way of working and want more than the status quo, Google Cloud is the new way to cloud

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Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI

Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how it can impact your business, and how to use it. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. We have brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost. This includes two learning paths that each feature comprehensive content: one is for the non-technical audience (introductory) ‘Introduction to Generative AI learning path’, and the other is for technical practitioners (more advanced) ‘Generative AI for Developers learning path’. Prefer less of a time commitment? Opt for individual courses and labs with the topics that suit your specific interest. Powered by Google Cloud technology, generative AI is reimagining the way that organizations do business by leveraging data. The use cases are far-ranging, from having smarter conversations with and creating rich experiences for customers, to enhancing business processes, to helping developers write code, and more. Google Cloud has been working for decades to bring AI technology solutions to organizations, and our tools make it easier to build experiences across our cloud portfolio. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI. Plus, labs give you direct access to Generative AI Studio and Vertex AI (Google Cloud’s machine learning platform) to get hands-on, technical experience. Get started with our training list below. No-cost foundational courses: no technical experience needed Don’t have a lot of time but want to learn the basics? Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. If getting started has you hungry to learn more, continue your no-cost learning to earn the Generative AI Fundamentals skill badge. It includes the course above, plus two other courses: ‘Introduction to Large Language Models’ and ‘Introduction to Responsible AI’. In total, this takes about two hours to complete and you’ll earn a digital skill badge to share on your resume and social channels to validate your foundational understanding of Google Cloud generative AI technology. Dig in deeper with our generative AI learning paths For a more comprehensive learning experience, choose from our two new gen AI learning paths — a mix of courses, labs and skill badges. Please note: we plan to add new trainings to this list, so keep checking back for more. Introduction to Generative AI - this path includes no-cost video courses. Content is non-technical, introductory, and designed for the tech-adjacent audience: roles like sales, HR, marketing and operations. Here is a look at the content: Introduction to Generative AI - course
Introduction to Large Language Models - course
Introduction to Responsible AI - course
Generative AI Fundamentals - earn a skill badge
Responsible AI: Applying AI Principals with Google Cloud - course Generative AI for Developers - this path includes a combination of technical hands-on labs and courses and requires Google Cloud credits to complete. It is designed for technical developers who work with gen AI, and includes the introductory training as a prerequisite. Content includes: Responsible AI for Developers - course
Introduction to Image Generation - course
Attention Mechanism - course
Encoder-Decoder Architecture - course
Transformer Models and BERT Model - course
Create Image Captioning Models - course
Introduction to Generative AI Studio - course
Generative AI Explorer: Vertex AI - earn a skill badge
Explore and Evaluate Models Using Model Garden - lab
Prompt Design Using PaLM - lab Gain technical experience with labs We currently offer these generative AI labs for anyone looking to get some hands-on experience by working directly in Google Cloud: Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes
Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes
Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes
Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes
Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes
Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes
Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Text embeddings with DeepLearning.AI and Andrew Ng We have brand new content built in collaboration with DeepLearning.AI, taught by Andrew Ng, founder of DeepLearning.AI and a globally recognized leader on the topic. Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” to learn various tasks like classification, outlier detection, text clustering, and semantic search. You’ll also learn to integrate them into LLM applications. Check out the video below to hear about these exciting opportunities found within text embeddings and generative AI. https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true New for technical practitioners: Intro to AI and ML Learn the big picture of how Google supports the data-to-AI journey and how generative AI is embedded in the AI development platform in our new course, Introduction to AI and Machine Learning on Google Cloud, designed mainly for technical practitioners — AI developers, data scientists, and ML engineers. The course covers the technologies, products and tools used to build an ML model, an ML pipeline, and a gen AI project based on the goals of data and AI practitioners. Made up of four primary modules that include labs and quizzes, this introductory course takes about six hours to complete and requires 12 credits. Stay on the cloud fast track with Innovators Plus Generative AI is  constantly evolving, and we’re developing new training materials to help you keep your career knowledge and skills up to date. An Innovators Plus subscription gives you access not only to generative AI content, but the full Google Cloud Skills Boost catalog of on-demand training for a whole year. You get $1,500 in developer benefits for only $299, including up to $1,000 in Google Cloud credits. Considering getting certified? You’ll also get an exam voucher, plus special access to Google Cloud experts and execs, live learning events, and more to round out your knowledge. Check out the details and sign up here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. Originally published here. Contributed by: Google Cloud Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how it can impact your business, and how to use it. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here . here We have brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost . This includes two learning paths that each feature comprehensive content: one is for the non-technical audience (introductory) ‘ Introduction to Generative AI learning path ’, and the other is for technical practitioners (more advanced) ‘ Generative AI for Developers learning path ’. Prefer less of a time commitment? Opt for individual courses and labs with the topics that suit your specific interest. brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost Introduction to Generative AI learning path Introduction to Generative AI learning path Generative AI for Developers learning path Generative AI for Developers learning path Powered by Google Cloud technology, generative AI is reimagining the way that organizations do business by leveraging data. The use cases are far-ranging , from having smarter conversations with and creating rich experiences for customers, to enhancing business processes, to helping developers write code, and more. use cases are far-ranging use cases are far-ranging Google Cloud has been working for decades to bring AI technology solutions to organizations, and our tools make it easier to build experiences across our cloud portfolio. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI . Plus, labs give you direct access to Generative AI Studio and Vertex AI (Google Cloud’s machine learning platform) to get hands-on, technical experience. Get started with our training list below. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI No-cost foundational courses: no technical experience needed Don’t have a lot of time but want to learn the basics? Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. Introduction to Generative AI Introduction to Generative AI If getting started has you hungry to learn more, continue your no-cost learning to earn the Generative AI Fundamentals skill badge . It includes the course above, plus two other courses: ‘Introduction to Large Language Models’ and ‘Introduction to Responsible AI’. In total, this takes about two hours to complete and you’ll earn a digital skill badge to share on your resume and social channels to validate your foundational understanding of Google Cloud generative AI technology. continue your no-cost learning Generative AI Fundamentals skill badge Generative AI Fundamentals skill badge two hours to complete Dig in deeper with our generative AI learning paths For a more comprehensive learning experience, choose from our two new gen AI learning paths — a mix of courses, labs and skill badges. Please note: we plan to add new trainings to this list, so keep checking back for more. Introduction to Generative AI - this path includes no-cost video courses . Content is non-technical, introductory, and designed for the tech-adjacent audience: roles like sales, HR, marketing and operations. Here is a look at the content: Introduction to Generative AI Introduction to Generative AI Introduction to Generative AI this path includes no-cost video courses Introduction to Generative AI - course Introduction to Large Language Models - course Introduction to Responsible AI - course Generative AI Fundamentals - earn a skill badge Responsible AI: Applying AI Principals with Google Cloud - course Introduction to Generative AI - course Introduction to Large Language Models - course Introduction to Responsible AI - course Generative AI Fundamentals - earn a skill badge Responsible AI: Applying AI Principals with Google Cloud - course Generative AI for Developers - this path includes a combination of technical hands-on labs and courses and requires Google Cloud credits to complete. It is designed for technical developers who work with gen AI, and includes the introductory training as a prerequisite. Content includes: Generative AI for Developers Generative AI for Developers Generative AI for Developers Responsible AI for Developers - course Introduction to Image Generation - course Attention Mechanism - course Encoder-Decoder Architecture - course Transformer Models and BERT Model - course Create Image Captioning Models - course Introduction to Generative AI Studio - course Generative AI Explorer: Vertex AI - earn a skill badge Explore and Evaluate Models Using Model Garden - lab Prompt Design Using PaLM - lab Responsible AI for Developers - course Introduction to Image Generation - course Attention Mechanism - course Encoder-Decoder Architecture - course Transformer Models and BERT Model - course Create Image Captioning Models - course Introduction to Generative AI Studio - course Generative AI Explorer: Vertex AI - earn a skill badge Explore and Evaluate Models Using Model Garden - lab Prompt Design Using PaLM - lab Gain technical experience with labs We currently offer these generative AI labs for anyone looking to get some hands-on experience by working directly in Google Cloud: Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes Get Started with Generative AI Studio Get Started with Generative AI Studio Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Getting Started Generative AI with Vertex AI: Getting Started Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Prompt Design Generative AI with Vertex AI: Prompt Design Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes Vertex AI PaLM API: Qwik Start Vertex AI PaLM API: Qwik Start Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes Integrate Search in Applications Using Gen AI App Builder Integrate Search in Applications Using Gen AI App Builder Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes Prompt Design using PaLM Prompt Design using PaLM Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Explore and Evaluate Models using Model Garden Explore and Evaluate Models using Model Garden Text embeddings with DeepLearning.AI and Andrew Ng We have brand new content built in collaboration with DeepLearning.AI , taught by Andrew Ng, founder of DeepLearning.AI and a globally recognized leader on the topic. Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” to learn various tasks like classification, outlier detection, text clustering, and semantic search. You’ll also learn to integrate them into LLM applications. Check out the video below to hear about these exciting opportunities found within text embeddings and generative AI. DeepLearning.AI DeepLearning.AI DeepLearning.AI DeepLearning.AI Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” “Understanding and Applying Text Embeddings with Vertex AI” “Understanding and Applying Text Embeddings with Vertex AI” https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true New for technical practitioners: Intro to AI and ML Learn the big picture of how Google supports the data-to-AI journey and how generative AI is embedded in the AI development platform in our new course, Introduction to AI and Machine Learning on Google Cloud , designed mainly for technical practitioners — AI developers, data scientists, and ML engineers. The course covers the technologies, products and tools used to build an ML model, an ML pipeline, and a gen AI project based on the goals of data and AI practitioners. Made up of four primary modules that include labs and quizzes, this introductory course takes about six hours to complete and requires 12 credits. Introduction to AI and Machine Learning on Google Cloud Introduction to AI and Machine Learning on Google Cloud this introductory course takes about six hours to complete and requires 12 credits. Stay on the cloud fast track with Innovators Plus Generative AI is  constantly evolving, and we’re developing new training materials to help you keep your career knowledge and skills up to date. An Innovators Plus subscription gives you access not only to generative AI content, but the full Google Cloud Skills Boost catalog of on-demand training for a whole year. You get $1,500 in developer benefits for only $299, including up to $1,000 in Google Cloud credits. Considering getting certified? You’ll also get an exam voucher, plus special access to Google Cloud experts and execs, live learning events, and more to round out your knowledge. Check out the details and sign up here . sign up here sign up here New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here . here Originally published here . here Contributed by: Google Cloud

Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level

This tutorial shows you how to deploy and automate a Docker container using Pulumi in Google Cloud Platform's Cloud Run with minimum permissions. Here's the GitHub Repository for reference: https://github.com/superjose/deploy-to-cloud-run-go Get the CLIs: . Create a Google Cloud Account Install Google Cloud CLI Install Pulumi CLI - We need it to build the Docker container. Install Docker Bootstrap the project: Create a directory: pulumi mkdir pulumi Run to initialize a pulumi project with Go (It can be your language of choice pulumi new go Navigate to the directory. pulumi cd pulumi Navigate to and create a new project. cloud.google.com Take note of the (Usually the project's name). project-id Generate the necessary permissions using gcloud CLI Login with the Google Auth CLI: gcloud auth login (Optional) Set the project in google cloud CLI (Can be changed anytime). This saves you from passing into every command. --project [PROJECT-ID] gcloud If your machine has multiple GCP projects, skip this step and pass the flag into every command. --project gcloud Create a service account (The account that Pulumi will connect to): gcloud iam service-accounts create pulumi-gcp --description="Pulumi GCP" Download the credentials for the service accounts and store them locally (Remember to replace with your GCP Project Id): [PROJECT-ID] gcloud iam service-accounts keys create ~/keys/gcp/pulumi-service-account-key-file.json --iam-account=pulumi-gcp@[PROJECT-ID].iam.gserviceaccount.com Set Pulumi's gcp credentials config path:
(This will connect the service account with Pulumi) pulumi config set gcp:credentials ~/keys/gcp/pulumi-service-account-key-file.json Set the GCP Project by doing: pulumi config set gcp:project [PROJECT-ID] Create a file (Inside the dir) and add the required permissions in : roles.gcp.yml pulumi includedPermissions # https://cloud.google.com/iam/docs/creating-custom-roles#creating
# Yaml to define the Pulumi GCP Roles that need to be created with gcloud CLI
title: Pulumi GCP Roles
description: |
 This policy ensures that all GCP roles are created using Pulumi.
stage: GA
# https://cloud.google.com/iam/docs/permissions-reference
includedPermissions:
 - serviceusage.services.list
 - serviceusage.services.enable
 - serviceusage.services.disable
 - serviceusage.services.get
 - serviceusage.services.use
 # Permissions for GCR
 - storage.objects.create
 - storage.objects.delete # Optional: only include if you need to delete images
 - storage.objects.get
 # Permissions for Google Artifact Registry
 - artifactregistry.repositories.create
 - artifactregistry.repositories.delete
 - artifactregistry.repositories.get
 - artifactregistry.repositories.list
 - artifactregistry.repositories.update
 - artifactregistry.repositories.downloadArtifacts
 - artifactregistry.repositories.uploadArtifacts
 - artifactregistry.repositories.deleteArtifacts
 # Permissions
 # Permissions
 - run.services.create
 - run.services.get
 - run.services.list
 - run.services.update
 - run.services.delete
 - run.services.getIamPolicy
 - run.services.setIamPolicy
 - iam.serviceAccounts.actAs
# NOTE: This should be removed the first time you're creating a role.
# This etag is to update the current active role (As GCP lets you manage multiple roles)
# I'm commenting it out so I can always replace the role
# etag: BwYS74Xx5y4= Create the with the file above:
(We assume we're running this code from the directory) pulumi_admin_role pulumi gcloud iam roles create pulumi_admin_role --project=[PROJECT-ID] --file='./roles.gcp.yml' In case you need to make edits, change the file and use: gcloud iam roles update pulumi_admin_role --project=[PROJECT-ID] --file='./roles.gcp.yml' We're also adding the role (I haven't found a better way) (Otherwise we'd get 403 errors when refreshing and updating in Pulumi)1 serviceAccountAdmin gcloud projects add-iam-policy-binding [PROJECT-ID] --role roles/iam.serviceAccountAdmin --member serviceAccount:pulumi-gcp@[PROJECT-ID].iam.gserviceaccount.com (The following will add the new role to the service account) gcloud projects add-iam-policy-binding [PROJECT-ID] --role projects/[PROJECT-ID]/roles/pulumi_admin_role --member serviceAccount:pulumi-gcp@[PROJECT-ID].iam.gserviceaccount.com Our in the directory:
(Check the code for comments!) main.go pulumi We enable the required services (Artifact Registry, and Cloud Run). Artifact Registry is used to host the docker container image. Cloud Runner will launch the Docker image from Artifact Registry. We build the docker image locally (We specify the platform in case you're using an ARM chip like M1, M2, Snapdragon SQ, X Elite, etc.) We create a chain of "DependsOn" to notify Pulumi:
5.1 Services need to be enabled first
5.2 We create the Artifact Repository
5.3 We build the docker image and push it to Artifact Registry.
5.4 We pull the Docker Image from Artifact Registry and run it.
5.5 We add IAM permissions so it can be accessed from anywhere. package main

import (
	"errors"
	"log"
	"os"

	"github.com/joho/godotenv"
	"github.com/pulumi/pulumi-docker/sdk/v3/go/docker"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/artifactregistry"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/cloudrun"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/projects"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

// This is the name you created in Google Cloud Platform (GCP).
const gcpProjectId = "deploy-to-cloud-run-go"

// The Docker Image Name
const dockerImageName = "my-app-docker"
const artifactRegistryServiceName = "artifact-registry-api"
const artifactRegistryRepoName = "my-app-artifact-repo"
const artifactRegistryRepoLocation = "us-east1"
const cloudRunAdminServiceName = "cloud-run-admin-service"
const cloudRunServiceName = "cloud-run-service"

// For more info: https://cloud.google.com/run/docs/locations
const cloudRunLocation = "us-east1"

// The tag for the Docker image
const imageTag = "latest"

// This is a url like: us-east1-docker.pkg.dev
// It is used to push the Docker image to Google Container Registry
// For more info: https://cloud.google.com/container-registry/docs/pushing-and-pulling
// The format is: <region>-docker.pkg.dev
var dockerGCPServer = cloudRunLocation + "-docker.pkg.dev"

// The full path to the Docker image
// It is used to deploy the Docker image to Google Cloud Run
// The format is: <region>-docker.pkg.dev/<project-id>/<repo-name>/<image-name>:<tag>
// For more info: https://cloud.google.com/run/docs/deploying
// Example: us-east1-docker.pkg.dev/deploy-to-cloud-run-go/my-app--artifact-repo/my-app-docker:latest
var dockerImageWithPath = dockerGCPServer + "/" + gcpProjectId + "/" + artifactRegistryRepoName + "/" + dockerImageName + ":" + imageTag

func main() {

	// Load the .env file
	err := godotenv.Load()

	if err != nil {
		log.Fatal("Error loading .env file")
	}

	pulumi.Run(func(ctx *pulumi.Context) error {

		enabledServices, serviceResultErr := enableServices(ctx)

		if serviceResultErr != nil {
			return serviceResultErr
		}

		artifactRegistryRepo, createArtifactErr := createArtifactRegistryNewRepository(ctx, &enabledServices)
		if createArtifactErr != nil {
			return createArtifactErr
		}

		dockerImage, buildAndPushErr := buildAndPushToContainerRegistry(ctx, &enabledServices, artifactRegistryRepo)

		if buildAndPushErr != nil {
			return buildAndPushErr
		}

		deployContainerErr := deployContainerToCloudRun(ctx, &enabledServices, dockerImage)

		if deployContainerErr != nil {
			return deployContainerErr
		}

		return nil
	})
}

type EnabledServices struct {
	CloudRunService *projects.Service `pulumi:"cloudRunService"`
	ArtifactRegistryService *projects.Service `pulumi:"artifactRegistryService"`
}

func enableServices(ctx *pulumi.Context) (EnabledServices, error) {

	cloudRunService, cloudRunAdminErr := projects.NewService(ctx, cloudRunAdminServiceName, &projects.ServiceArgs{
		Service: pulumi.String("run.googleapis.com"),
		Project: pulumi.String(gcpProjectId),
	})

	if cloudRunAdminErr != nil {
		return EnabledServices{}, cloudRunAdminErr
	}

	artifactRegistryService, err := projects.NewService(ctx, artifactRegistryServiceName, &projects.ServiceArgs{
		Service: pulumi.String("artifactregistry.googleapis.com"),
	})

	if err != nil {
		return EnabledServices{}, err
	}
	return EnabledServices{
		CloudRunService: cloudRunService,
		ArtifactRegistryService: artifactRegistryService,
	}, nil
}

func createArtifactRegistryNewRepository(ctx *pulumi.Context, enabledServices *EnabledServices) (*artifactregistry.Repository, error) {

	if enabledServices == nil || enabledServices.ArtifactRegistryService == nil {
		return nil, errors.New("enabledServices cannot be nil")
	}

	dependingResources := []pulumi.Resource{
		enabledServices.ArtifactRegistryService,
	}

	repo, err := artifactregistry.NewRepository(ctx, artifactRegistryRepoName, &artifactregistry.RepositoryArgs{
		Location: pulumi.String(artifactRegistryRepoLocation),
		RepositoryId: pulumi.String(artifactRegistryRepoName),
		Format: pulumi.String("DOCKER"),
		Description: pulumi.String("The repository that will hold social-log Docker images."),
	}, pulumi.DependsOn(dependingResources))

	if err != nil {
		return nil, err
	}

	return repo, nil
}

func buildAndPushToContainerRegistry(ctx *pulumi.Context, enabledServices *EnabledServices, artifactRegistryRepo *artifactregistry.Repository) (*docker.Image, error) {

	if enabledServices == nil || enabledServices.ArtifactRegistryService == nil {
		return nil, errors.New("enabledServices cannot be nil")
	}

	if artifactRegistryRepo == nil {
		return nil, errors.New("artifactRegistryRepo cannot be nil")
	}

	// Lookup GOOGLE_CREDENTIALS environment variable which should hold the path to the JSON key file
	jsonKeyPath, present := os.LookupEnv("GOOGLE_CREDENTIALS_FILE_PATH")
	if !present {
		return nil, errors.New("GOOGLE_CREDENTIALS_FILE_PATH environment variable is not set")
	}

	// Read the JSON key file
	jsonKey, err := os.ReadFile(jsonKeyPath)
	if err != nil {
		return nil, err
	}

	dependingSources := []pulumi.Resource{
		enabledServices.ArtifactRegistryService,
		artifactRegistryRepo,
	}

	// Build and push Docker image to Google Container Registry using the JSON key
	image, err := docker.NewImage(ctx, dockerImageName, &docker.ImageArgs{
		Build: &docker.DockerBuildArgs{
			Context: pulumi.String("../"), // Adjust the context according to your project structure

			ExtraOptions: pulumi.StringArray{
				// This option is needed for devices running on ARM architecture, such as Apple M1/M2/MX CPUs
				pulumi.String("--platform=linux/amd64"),
			},
		},
		ImageName: pulumi.String(dockerImageWithPath),
		Registry: &docker.ImageRegistryArgs{
			Server: pulumi.String(dockerGCPServer),
			Username: pulumi.String("_json_key"), // Special username for GCP
			Password: pulumi.String(string(jsonKey)), // Provide the contents of the key file
		},
	}, pulumi.DependsOn(dependingSources))
	if err != nil {
		return nil, err
	}

	return image, nil
}

func deployContainerToCloudRun(ctx *pulumi.Context, enabledServices *EnabledServices, dockerImage *docker.Image) error {

	if enabledServices == nil || enabledServices.CloudRunService == nil {
		return errors.New("enabledServices cannot be nil")
	}

	if dockerImage == nil {
		return errors.New("dockerImage cannot be nil")
	}

	dependingSources := []pulumi.Resource{
		enabledServices.CloudRunService,
		dockerImage,
	}

	appService, err := cloudrun.NewService(ctx, cloudRunServiceName, &cloudrun.ServiceArgs{
		Location: pulumi.String(cloudRunLocation), // Choose the appropriate region for your service
		Template: &cloudrun.ServiceTemplateArgs{
			Spec: &cloudrun.ServiceTemplateSpecArgs{
				Containers: cloudrun.ServiceTemplateSpecContainerArray{
					&cloudrun.ServiceTemplateSpecContainerArgs{
						Image: dockerImage.ImageName,
						Resources: &cloudrun.ServiceTemplateSpecContainerResourcesArgs{
							Limits: pulumi.StringMap{
								"memory": pulumi.String("256Mi"), // Adjust the memory limit as needed
							},
						},
					},
				},
			},
		},
		Traffics: cloudrun.ServiceTrafficArray{
			&cloudrun.ServiceTrafficArgs{
				Percent: pulumi.Int(100),
				LatestRevision: pulumi.Bool(true),
			},
		},
	}, pulumi.DependsOn(dependingSources))

	if err != nil {
		return err
	}

	_, iamErr := cloudrun.NewIamMember(ctx, "invoker", &cloudrun.IamMemberArgs{
		Service: appService.Name,
		Location: appService.Location,
		Role: pulumi.String("roles/run.invoker"),
		Member: pulumi.String("allUsers"),
	})

	if iamErr != nil {
		return iamErr
	}

	ctx.Export("containerUrl", appService.Statuses.Index(pulumi.Int(0)).Url().ToOutput(ctx.Context()))
	return nil
} Update the in your Dockerfile:
There's a , and in which you need to export your environment variable to ENV known issue here HOME /root # Set the ENV HOME before your ENTRYPOINT.
ENV HOME=/root

# This is specific to your project. 
ENTRYPOINT ["/whatever-is-your-entrypoint"] Create a .env file in the pulumi directory: Set the * to the one you saved on full path 11) GOOGLE_CREDENTIALS_FILE_PATH="/Users/myusername/keys/gcp/pulumi-service-account-key-file.json" Run pulumi up You should be up and running! Extras The go.mod module social-log-go

go 1.21

toolchain go1.22.0

require (
	github.com/pulumi/pulumi-gcp/sdk/v7 v7.11.2
	github.com/pulumi/pulumi/sdk/v3 v3.108.1
)

require (
	dario.cat/mergo v1.0.0 // indirect
	github.com/Masterminds/semver v1.5.0 // indirect
	github.com/Microsoft/go-winio v0.6.1 // indirect
	github.com/ProtonMail/go-crypto v0.0.0-20230828082145-3c4c8a2d2371 // indirect
	github.com/aead/chacha20 v0.0.0-20180709150244-8b13a72661da // indirect
	github.com/agext/levenshtein v1.2.3 // indirect
	github.com/apparentlymart/go-textseg/v13 v13.0.0 // indirect
	github.com/atotto/clipboard v0.1.4 // indirect
	github.com/aymanbagabas/go-osc52/v2 v2.0.1 // indirect
	github.com/blang/semver v3.5.1+incompatible // indirect
	github.com/charmbracelet/bubbles v0.16.1 // indirect
	github.com/charmbracelet/bubbletea v0.24.2 // indirect
	github.com/charmbracelet/lipgloss v0.7.1 // indirect
	github.com/cheggaaa/pb v1.0.29 // indirect
	github.com/cloudflare/circl v1.3.7 // indirect
	github.com/containerd/console v1.0.4-0.20230313162750-1ae8d489ac81 // indirect
	github.com/cyphar/filepath-securejoin v0.2.4 // indirect
	github.com/djherbis/times v1.5.0 // indirect
	github.com/emirpasic/gods v1.18.1 // indirect
	github.com/go-git/gcfg v1.5.1-0.20230307220236-3a3c6141e376 // indirect
	github.com/go-git/go-billy/v5 v5.5.0 // indirect
	github.com/go-git/go-git/v5 v5.11.0 // indirect
	github.com/gogo/protobuf v1.3.2 // indirect
	github.com/golang/glog v1.1.0 // indirect
	github.com/golang/groupcache v0.0.0-20210331224755-41bb18bfe9da // indirect
	github.com/golang/protobuf v1.5.3 // indirect
	github.com/google/uuid v1.3.0 // indirect
	github.com/grpc-ecosystem/grpc-opentracing v0.0.0-20180507213350-8e809c8a8645 // indirect
	github.com/hashicorp/errwrap v1.1.0 // indirect
	github.com/hashicorp/go-multierror v1.1.1 // indirect
	github.com/hashicorp/hcl/v2 v2.17.0 // indirect
	github.com/inconshreveable/mousetrap v1.1.0 // indirect
	github.com/jbenet/go-context v0.0.0-20150711004518-d14ea06fba99 // indirect
	github.com/joho/godotenv v1.5.1 // indirect
	github.com/kevinburke/ssh_config v1.2.0 // indirect
	github.com/lucasb-eyer/go-colorful v1.2.0 // indirect
	github.com/mattn/go-isatty v0.0.19 // indirect
	github.com/mattn/go-localereader v0.0.1 // indirect
	github.com/mattn/go-runewidth v0.0.15 // indirect
	github.com/mitchellh/go-ps v1.0.0 // indirect
	github.com/mitchellh/go-wordwrap v1.0.1 // indirect
	github.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6 // indirect
	github.com/muesli/cancelreader v0.2.2 // indirect
	github.com/muesli/reflow v0.3.0 // indirect
	github.com/muesli/termenv v0.15.2 // indirect
	github.com/opentracing/basictracer-go v1.1.0 // indirect
	github.com/opentracing/opentracing-go v1.2.0 // indirect
	github.com/pgavlin/fx v0.1.6 // indirect
	github.com/pjbgf/sha1cd v0.3.0 // indirect
	github.com/pkg/errors v0.9.1 // indirect
	github.com/pkg/term v1.1.0 // indirect
	github.com/pulumi/appdash v0.0.0-20231130102222-75f619a67231 // indirect
	github.com/pulumi/esc v0.6.2 // indirect
	github.com/pulumi/pulumi-docker/sdk/v3 v3.6.1 // indirect
	github.com/rivo/uniseg v0.4.4 // indirect
	github.com/rogpeppe/go-internal v1.11.0 // indirect
	github.com/sabhiram/go-gitignore v0.0.0-20210923224102-525f6e181f06 // indirect
	github.com/santhosh-tekuri/jsonschema/v5 v5.0.0 // indirect
	github.com/sergi/go-diff v1.3.1 // indirect
	github.com/skeema/knownhosts v1.2.1 // indirect
	github.com/spf13/cobra v1.7.0 // indirect
	github.com/spf13/pflag v1.0.5 // indirect
	github.com/texttheater/golang-levenshtein v1.0.1 // indirect
	github.com/tweekmonster/luser v0.0.0-20161003172636-3fa38070dbd7 // indirect
	github.com/uber/jaeger-client-go v2.30.0+incompatible // indirect
	github.com/uber/jaeger-lib v2.4.1+incompatible // indirect
	github.com/xanzy/ssh-agent v0.3.3 // indirect
	github.com/zclconf/go-cty v1.13.2 // indirect
	go.uber.org/atomic v1.9.0 // indirect
	golang.org/x/crypto v0.17.0 // indirect
	golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa // indirect
	golang.org/x/mod v0.14.0 // indirect
	golang.org/x/net v0.19.0 // indirect
	golang.org/x/sync v0.5.0 // indirect
	golang.org/x/sys v0.15.0 // indirect
	golang.org/x/term v0.15.0 // indirect
	golang.org/x/text v0.14.0 // indirect
	golang.org/x/tools v0.15.0 // indirect
	google.golang.org/genproto/googleapis/rpc v0.0.0-20230706204954-ccb25ca9f130 // indirect
	google.golang.org/grpc v1.57.1 // indirect
	google.golang.org/protobuf v1.31.0 // indirect
	gopkg.in/warnings.v0 v0.1.2 // indirect
	gopkg.in/yaml.v3 v3.0.1 // indirect
	lukechampine.com/frand v1.4.2 // indirect
) If you include this Run , and this will fetch all the packages for you go.mod go tidy Footnotes I fought against permissions for 5 days. The predefined GCP role brought in the additional permissions needed. 1* serviceAccountAdmin

Automate and Deploy a Docker Container to Google Cloud Run from Scratch Using Pulumi and Go

As a backend developer, I often find myself collaborating closely with my frontend team to ensure the seamless deployment of web applications. Recently, our frontend team brought forth a pressing challenge that was slowing down our development workflow. Problem Statement 🤔 They were responsible for managing a Flutter web application running on a Google Cloud Platform (GCP) virtual machine (VM). While the app itself was impressive, the deployment process left much to be desired. The existing deployment workflow was a multi-step ordeal: Build the Flutter web app. Upload the app to the VM. Unzip the uploaded package. Extract the contents into the appropriate Apache directory. And sometimes, things went awry, causing deployment to drag on for far too long. Initial Attempt with Docker Image Deployment 🐳 Our initial approach was to implement Docker image deployment on the VM using GitHub Actions. While this seemed promising, it quickly became apparent that it came with its own set of complexities and challenges. 🤯 The process of creating Docker images occasionally led to failures, leaving us scratching our heads. 🔒 SSH connections to the VM sometimes failed unexpectedly, causing frustrating delays in the deployment process. ⌛ Timeouts became a common occurrence, further exacerbating the deployment woes. It was clear that our initial attempt was far from the seamless deployment solution we were aiming for. Cloud Run The Saviour ☁️ In our quest to find a more efficient deployment solution and streamline collaboration, we stumbled upon Cloud Run—a game-changer in our deployment journey. Here are some key features we harnessed: Auto Deployment from GitHub 🚀 Seamless integration with GitHub. Cloud Build triggers for automatic builds and deploys. Containerization Made Easy 🐳 Dockerfile for defining environment and dependencies. Automatic container building and deployment. Auto-Scaling for Efficiency ⚖️ Dynamic scaling based on incoming requests. Cost-effective resource allocation. Fetching Code from GitHub 🔄 Real-time collaboration among team members. Automatic deployment of pushed changes. Cost Efficiency 💰 Pay-as-you-go pricing model. No charges for idle time. Robust Monitoring and Logging 📊 Detailed logs and metrics. Integration with monitoring tools like Stackdriver. 😜 Solution - Implementation: Now let's jump into the actual solution which worked for us, adding all the templates that worked for us so you can also utilize the same or modify it according to your needs. In this solution phase we will go through Docker setup for your project Apache and server file setup to smooth running of you application in cloud run And at the end the cloud run setup. Step 1: Setting Up Docker in Repo Add this docker file to you existing repo # Stage 1: Build the application
FROM dart:stable AS build

# Install dependencies for Flutter
RUN apt-get update && apt-get install -y curl git unzip xz-utils zip libglu1-mesa

# Clone the Flutter repository
RUN git clone https://github.com/flutter/flutter.git /usr/local/flutter

# Set the Flutter path
ENV PATH="/usr/local/flutter/bin:/usr/local/flutter/bin/cache/dart-sdk/bin:${PATH}"

# Checkout the specific version of Flutter
RUN flutter channel stable
RUN flutter upgrade
RUN git -C /usr/local/flutter checkout 1f6bdb6

#above after checkout is commit hash which indicates the version of flutter
#to learn more about flutter versioning, please visit https://flutter.dev/docs/development/tools/sdk/releases?tab=linux

# Verify Flutter installation
RUN flutter doctor

# Set working directory
WORKDIR /app

# Copy over your app
COPY pubspec.* /app/
RUN flutter pub get
COPY . /app
RUN flutter pub get --offline
RUN flutter clean
RUN flutter build web --release --no-tree-shake-icons

# Stage 2: Create the runtime environment with Apache
FROM httpd:alpine

# Install gettext for envsubst
RUN apk add --no-cache gettext

# Copy the built app to the Apache server directory
COPY --from=build /app/build/web/ /usr/local/apache2/htdocs/

# Expose port
EXPOSE 8080

# Copy the Apache configuration template and startup script
COPY httpd.conf.template /usr/local/apache2/conf/httpd.conf.template

# Add a shell script to start Apache with the right PORT
COPY start-apache.sh /usr/local/bin/start-apache.sh
RUN chmod +x /usr/local/bin/start-apache.sh

# Start Apache using the startup script
CMD ["start-apache.sh"] Above docker file is multi stage In first stage we download dependency of flutter and generate the build In second stage we setup apache to host this as static site in cloud run Now add a apache configuration file to make port dynamic as cloud run expect an open port configuration for application # this should go in directory with httpd.conf.template

ServerRoot "/usr/local/apache2"

Listen ${PORT}

LoadModule mpm_event_module modules/mod_mpm_event.so
LoadModule authn_file_module modules/mod_authn_file.so
LoadModule authn_core_module modules/mod_authn_core.so
LoadModule authz_host_module modules/mod_authz_host.so
LoadModule authz_groupfile_module modules/mod_authz_groupfile.so
LoadModule authz_user_module modules/mod_authz_user.so
LoadModule authz_core_module modules/mod_authz_core.so
LoadModule access_compat_module modules/mod_access_compat.so
LoadModule auth_basic_module modules/mod_auth_basic.so
LoadModule reqtimeout_module modules/mod_reqtimeout.so
LoadModule filter_module modules/mod_filter.so
LoadModule mime_module modules/mod_mime.so
LoadModule log_config_module modules/mod_log_config.so
LoadModule env_module modules/mod_env.so
LoadModule headers_module modules/mod_headers.so
LoadModule setenvif_module modules/mod_setenvif.so
LoadModule version_module modules/mod_version.so
LoadModule unixd_module modules/mod_unixd.so
LoadModule status_module modules/mod_status.so
LoadModule autoindex_module modules/mod_autoindex.so
LoadModule dir_module modules/mod_dir.so
LoadModule alias_module modules/mod_alias.so
LoadModule proxy_html_module modules/mod_proxy_html.so

<IfModule unixd_module>
User daemon
Group daemon
</IfModule>

ServerAdmin you@example.com

<Directory />
 AllowOverride none
 Require all denied
</Directory>

<Directory "/usr/local/apache2/htdocs">
 Options Indexes FollowSymLinks
 AllowOverride None
 Require all granted
</Directory>

<IfModule dir_module>
 DirectoryIndex index.html
</IfModule>

<Files ".ht*">
 Require all denied
</Files>

ErrorLog /proc/self/fd/2

LogLevel warn

<IfModule log_config_module>
 LogFormat "%h %l %u %t \"%r\" %>s %b \"%{Referer}i\" \"%{User-Agent}i\"" combined
 LogFormat "%h %l %u %t \"%r\" %>s %b" common
 CustomLog /proc/self/fd/1 common
</IfModule>

<IfModule alias_module>
 ScriptAlias /cgi-bin/ "/usr/local/apache2/cgi-bin/"
</IfModule>

<Directory "/usr/local/apache2/cgi-bin">
 AllowOverride None
 Options None
 Require all granted
</Directory>

<IfModule headers_module>
 RequestHeader unset Proxy early
</IfModule>

<IfModule mime_module>
 TypesConfig conf/mime.types
 AddType application/x-compress .Z
 AddType application/x-gzip .gz .tgz
</IfModule>

<IfModule mime_magic_module>
 MIMEMagicFile conf/magic
</IfModule>

Include conf/extra/proxy-html.conf Add the just add server file with start-apache.sh name to add an script to run server smoothly #!/bin/sh
# Substitute environment variables in Apache configuration
envsubst < /usr/local/apache2/conf/httpd.conf.template > /usr/local/apache2/conf/httpd.conf

# For debugging: print the PORT variable and the resulting httpd.conf
echo "PORT: $PORT"
# cat /usr/local/apache2/conf/httpd.conf

# Start Apache in the foreground
exec httpd -DFOREGROUND Above file add port from env using envsubst to and replace actual apache config. Now after adding all these just try by running application locally for testing use below command to verify 1 docker build -t my-flutter-app . -> For building
2 docker run -d -p 8080:8080 -e PORT=8080 my-flutter-app -> To run after builiding If all above works fine push it to github. Step 2: Setting Up Cloud Run Begin by navigating to the Google Cloud Console and creating a new project or selecting an existing one. In the Cloud Console, activate the Cloud Run API for your project. You will be able to see the cloud run here something like in below image. Now create cloud run service Fill the details and select the cloud build as option Select the Repo and Branch and setup docker file in option Now click on save and that’s it. Now build will start and you will be able to see it in cloud build section. At the end after successful build you can check you web application on external ip provided by cloud run 🚗 The Journey This blog captures our journey from a challenging deployment process to a streamlined and efficient solution using Cloud Run. With features like auto deployment, containerization, auto-scaling, and cost efficiency, Cloud Run has become a vital tool in our development arsenal. Say goodbye to deployment woes and embrace Cloud Run for a smoother development experience. ☁️🚀

Supercharge Your Flutter Web App: Deploy with Ease on Google Cloud Run

Let's learn about via these 68 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Google Cloud Platform /Learn Repo 1. How to Pass the Certified Kubernetes Administrator Exam in 7 Days A couple of weeks ago I passed the Certified Kubernetes Administrator (CKA) exam with 7 days of prep. Here’s how I did it. 2. I Will Never Host My Apps on a Server Again TL;DR As a solo entrepreneur I cannot manage a server myself. PaaS comes in as a life saver. Since I started hosting my apps on Google App Engine I saved time and money, but the best part is: my app is secure and scalable without having to lift a finger. 3. Should You Even Use A Regular Web Hosting Anymore? Nowadays, almost all the websites that are out there are using just regular web hosting. That seems pretty normal, but in days where cloud hosting companies like Amazon Web Services and Google Cloud are getting very popular, the question that comes in mind is Should we even use regular web hosting anymore or we should turn to the cloud. Just think about it, they’re often much cheaper and have support technologies that regular web hostings don’t. 4. Important Differences Between Cloud-Based, Cloud-Enabled, And Cloud-Native Apps Learn the difference between Cloud-Based, Cloud-Enabled, and Cloud-Native Applications. 5. 13 Questions and Answers for Google Cloud Reference Architectures Google Cloud is a cloud computing platform that can be used to build and deploy applications.  It allows you to take advantage of the flexibility of development while scaling the infrastructure as needed. 6. Hundreds of Google and Amazon Employees Oppose the Israeli "Nimbus" Cloud Project Hundreds of Google and Amazon employees anonymously sign a letter opposing the tech giants’ deal with Israel. 7. 5 Best Google Cloud Professional Data Engineer Certification Exam Courses in 2022 In this article, I will share the best online courses to help you pass the Google Cloud Professional Data Engineer certification exam. 8. 25 Products Google Sent To Their Graveyard: A Brief History Google is generally at innovation’s forefront, and a large number of its items and administrations have become industry pioneers. Be that as it may, not all things google conjures up goes to gold. The tech goliath has created a couple of clunkers, yet Google doesn’t stop for a second to cut its misfortunes. Here’s a voyage through the Google Graveyard, with a glance at certain items and administrations Google has executed off as the years progressed. 9. AWS/Azure/Google Certifications are More Important than Linux Certs Skip the Linux certs and go with AWS/Azure/Google certification instead. These days, pretty much every cloud provider has their own version of Linux. 10. Mount file to Kubernetes Pod Without Deleting the Existing File What I learned about Kubernetes when trying to mount config maps into a docker container that already have an existing file in the same directory. 11. Today I Learned: Pull Docker Image from GCR (Google Container Registry) in any non-GCP Kubernetes… How to a pull Docker Image from GCR in any non-GCP Kubernetes cluster 12. Google Anthos Vs. Cast.AI: A Review The idea that you can build an app in one place and then deploy it across multiple clouds is an enticing one. 13. Start a Tech Startup for Free with Google Cloud Platform Learn how to piece together Google Cloud Platform (GCP) services ($300 credit on signup) and other services to start your tech startup with this no-fuss guide. 14. How I Built a Warehouse Management App Called Cardus A web app that allows users to create and manage their warehouse of items. 15. Trying to Sign Up for a GCP Account: My Experience Gareth Dwyer explains what it was like to try to sign up for a GCP account. 16. From a Googler’s Scratchpad: How to Prepare for a Google Job Interview Cracking interviews at Google, Amazon, Microsoft, Meta - starts from Self Reflection. A structured & iterative approach to do this exercise is beneficial! 17. How to Setup WordPress Website on Google Cloud For Free Using Google Cloud Platform and Cloudflare is a budget solution to securely and optimally host a small and fully featured WordPress website. 18. 6 Cloud Migration Benefits for Businesses Cloud migration benefits the application by opening a new horizon for high-level architecture applications that capture and process data in real-time. A smooth cloud migration experience and hassle-free post-migration efficiency are assured by properly implemented cloud migration strategies. 19. 2023 Will Be the Year of Kubernetes (and Other Predictions in the Cloud and Infrastructure Industry) Dive into the new year’s top cloud and infra-tech trends, straight from a cloud and infrastructure technology fanatic. 20. CAST AI Extends Support to AWS, Azure, and GCP After Raising $10M CAST AI platform that optimizes cloud spend now supports all three major cloud service providers: Amazon Web Services, Google Cloud Platform, and Azure. 21. Hack Your Way to LookML Mastery By Following These Tips Tips to help BI developers create a seamless pipeline in Google's visualization tool Looker. 22. Pillar of Heaven : Linux and the Cloud Computing ‘Re-evolution’ Importance of Linux in the Cloud Computing revolution 23. Cloud Migration: Automated Discovery Tools And Their Benefits With the advent of new tools, automation and orchestration are having an immense impact on easing the burden of moving to the cloud. 24. Why You Should Think About Switching To The Cloud Hosting today Cloud computing has become essential for most homes and businesses, and it has been critical in reducing social upheaval and economic devastation. 25. 5 Ways DevOps can make IT Infrastructure more Scalable DevOps, did you even hear about it in the first place? Or is it familiar? Before we go to learning the 5 Ways DevOps can make IT Infrastructure more Scalable, we must explore each term and practices around it first. 26. 5 Ways to Protect Your Cloud Storage The days of thumb drives are slowly passing us by because cloud-based storage solutions are here to stay. Services like Google Drive and Dropbox store your data on the web and let you access them at any place and time. As long as you have access to the internet that is. But in this day and age, who doesn’t right? 27. Weekly Web3 Roundup Despite the bear market, brands, IP holders, and artists are building the future of the internet. Read our weekly update to stay up-to-date on everything web3. 28. How To Successfully Migrate Data From DynamoDB To Bigtable And Spanner Being informed and relevant aren’t enough to catch a moving target that’s disappearing every 24 hours. 29. How to Use the Google Sheets API with Python Tutorial describing the steps to configure a Google Cloud Platform project to allow programmatic integration with the Google Sheets API via Python. 30. The Top 5 DevOps Tools and Services to Consider as a Startup A collection of DevOps tools can significantly enhance your Software Development Life Cycle (SDLC) and increase engineering productivity. 31. How to Deploy & Monitor Honeypots on GCP with Kibana [Tutorial] One of my favourite areas of cybersecurity is SIEM (Security Incident Event Management). In 2017 I wrote a post on how I got a role in cyber security, one of my recommendations was using the Elastic Stack as a SIEM as a start-off point for those looking to understand log analysis and how to investigate incidents. But one of the main gripes people had was, where can they get data to work on in their home environments. This post will focus on setting up a honeypot that already utilises the ELK Stack… 32. Top 5 Cloud Migration Strategies You Need to Know A cloud migration strategy is a plan a company can adopt to move some or all data and systems into the cloud. 33. How To Reduce Risks And Prepare For The Unknown To be Always On in uncertain times, mundane must be your new black. 34. Managing Encryption at Scale with Envelope Encryption Envelope encryption is a good way of maintaining your cybersecurity at scale, without tying up a significant portion of your resources on less efficient methods 35. Cloud Architects Need More than Certifications Are you AWS certified, Azure certified, or Google certified and struggling to get your first cloud architect or solution architect job? If so, I have some information that can help you. 36. AWS, Azure and GCP Pricing: The True Cost of DevOps To make the choice between 3 market giants (Azure, AWS, and Google Cloud) clearer, we've conducted research on the pricing plans for DevOps services. 37. So Safe You'll Cloud 9 Cloud migration means to move business data, applications, or other critical business services from on-premise data centers or onsite computers to the cl 38. Using DAL For Migration From DynamoDB to Spanner and Bigtable Game changers emerge when you are deep in the trenches laying the foundation. 39. How to Reduce the Chances of an Outage An outage (also known as downtime) is a period of time when a given service or system is unavailable, failing to provide and perform its primary functionality. 40. A Guide to Authoring Power BI Reports on Real-Time Google Sheets Data CData Power BI Connectors provide self-service integration with Microsoft Power BI. The CData Power BI Connector for Google Sheets links your Power BI reports to real-time Google Sheets data. You can monitor Google Sheets data through dashboards and ensure that your analysis reflects Google Sheets data in real-time by scheduling refreshes or refreshing on demand. This article details how to use the Power BI Connector to create real-time visualizations of Google Sheets data in Microsoft Power BI Desktop. 41. Top 5 Reasons Why Companies are Moving to the Cloud Cloud has introduced some new business capabilities through cost-effectiveness or accessibility, as a company you don't want to miss the opportunity. 42. How to Select the Best Container Registry If you are developing any type of containerized application, there will be some concerns for you as a developer or an ops engineer. One such major concern is where to store the container images so that they can be easily and securely accessible at the deployment. This is where container registries come into play by offering a place to store these images. Therefore, selecting the best possible container registry for your specific use case is vital in any containerized application development. Yet, how can we ensure that we select the best possible registry? Let's try to answer that question in this post. 43. Hey Google, Where's the Search? Just some of the things that could be easily fixed to make the Google Cloud Platform and Workspace easier to use. 44. Collecting and Shipping Windows Events Logs with OpenTelemetry If you use Windows, you want to monitor Windows Events. I'll show you how to easily monitor Windows Events with OpenTelemetry. 45. Cloud Solutions Propelled Into The Spotlight Courtesy Of Covid-19 In the wake of the COVID-19 pandemic, cloud service solutions have been thrown into the limelight as companies and organisations across the globe grapple with the rapid shift to remote working and learning. With the widespread closure of non-essential organisations and businesses forcing organisations’ leaders to consider new and innovative approaches to shifting their businesses online, the move to cloud computing has become a far greater priority than ever before. The industry statistics demonstrate this: according to new figures from analyst firm Gartner, by the end of 2020 we will have seen the global public cloud services market reach $266.4 billion, up from $227.8 billion in 2019. 46. AWS vs. Azure vs. Google Cloud: Which One Delivers Most Value for the Price How to choose the right cloud provider for your business? Read this updated 2021 pricing comparison for AWS vs. Azure vs. Google Cloud. 47. How to Install Anthos Bare Metal on Intel NUCs: A Beginner's Guide Anthos on Bare Metal allows you to spin up a cluster from as few as 2 Intel NUCs. It’s a relatively straightforward process. 48. 6 Cloud Predictions for the Near Future Looking forward to the new decade, it would be tempting to speculate where the cloud industry could be heading. With tech developments in the cloud shop, by 2022, sectors like AI-Web, IoT, Blockchain, Space Cloud Computing, etc. are assumed to reach new heights. 49. Cloud Cost Management Vs. Cloud Cost Optimization: Which is Better? Cloud cost management doesn’t guarantee savings, automated optimization does. 50. Building Sustainable AI/ML Solutions in the Cloud with Federated Learning Compared to centralized training and cooling mechanisms adopted at data centers, how can Federated Learning help us combat detrimental environmental impacts? 51. Gonna fly now: why I've left Silicon Valley's lions for Vultr Do you ever feel like an underdog? And that sometimes all that’s left is scraps?
If you can relate, then I think you’ll enjoy my Silicon Valley career story. 52. What is Cloud Gaming and How is Google Leading the Industry? Most games today are downloaded and played on smartphones, gaming consoles, and PCs. Network connections support player accounts and in-game purchases, but the games themselves run on players' devices. Game companies build services around top game titles, enabling them to deliver continuous updates that add new gameplay and respond to the public feedback on social streaming services. 53. Exporting Your GKE Cluster to Terraform Cloud: A Guide with Challenges and Solutions In this article, I'll share our journey at ANNA Money from using our Kubernetes installation in GCP to the managed Kubernetes service by Google and GKE. 54. Open Source Worse Practices: Serverless Inc. as an Example of What Can Go Wrong What can go wrong when you don’t keep up with your developer community 55. 3 Risk-Mitigation Lessons That We Learned The Hard Way This Year What do in-flight refueling maneuvers and cloud-to-cloud migration have in common? 56. Serverless Python Google Cloud Platform: Hosting Django on Cloud Run Learn about the basics of Serverless, Docker and deploying a Django (Python) application to Cloud Run, a serverless offering by Google Cloud Platform 57. Use Declarative Cloud Functions In Your Serverless Apps To Control Size of Codebase After more than two years of building serverless applications on Firebase and writing countless Cloud Functions, I realised a lot of it began to repeat. 58. Get a Free Week on Cloud Academy To Kickstart Your Tech Training Are you looking to make a jump in your technical career? Want to get trained or certified on AWS, Azure, Google Cloud Platform, DevOps, Kubernetes, Python, or another in-demand skill? 59. I Found A Painless Way To Manage Secrets In Google Kubernetes Engine When deploying complex SaaS platforms, secrets can quickly become the bane of one’s existence. I once wrote about how we ran out of space for our environment variables (in Elastic Beanstalk) because we had so many API keys and secrets. But those were the bad old days. 60. Making Hybrid and Multi-cloud Applications a Reality with Anthos Recently, Google launched Anthos platform in its Cloud Next Conference and since then it has been the talk of the town! 61. The Best Cloud Computing Programming Languages To Learn Learning cloud programming is the need of the hour in this era of technology.  Here is the list of the best cloud computing programming languages. 62. YubiKey Introduction: GCP Service Account Key Generator Or how to stop leakage of your keys once and for all 63. Good and Not So Good Things With Firebase Most of the developers have heard for Google’s product called Firebase. It’s, how Google says “mobile platform that helps you quickly develop high-quality apps and grow your business.“. It’s basically a set of tools that most developers will need when building an app. In this article I’ll go over these tools, and point all the things you need to know if you choose to use Firebase. 64. How to Migrate From GCM To FCM Reinforcing Google’s decision made about deprecating the Google Cloud Messaging on 11th April 2019, everyone is required to migrate from GCM to FCM to ensure smoother push notification to the subscribed users. And, the migration part requires seamless efforts and technical expertise to carry out. So, this blog will unfold all the terms pertaining to GCM to FCM migration 65. Evaluating Cloud Backup Solutions: AWS vs. Azure vs. Google Cloud Source: Pixabay 66. Comparing Container Pipelines Introduction 67. GitHub Actions: How To Deploy To Google Kubernetes Engine GitHub actions and workflows are great. We use them to deploy a web app to different cluster environments and I want to show you how we did it. Hopefully, this helps to simplify your deployment process as well. I’ve also written a companion article that describes our GitHub workflows for continuous integration. 68. Anthos on Bare Metal: How to Manage a Private Cluster From the Cloud The millions of devices that currently make up the Internet of Things (IoT) reside not in the cloud but on-premises: from retail stores to factory floors. Thank you for checking out the 68 most read stories about Google Cloud Platform on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

68 Stories To Learn About Google Cloud Platform

Build a Document Analyzer with ChatGPT, Google Cloud, and Python

চ্যাটজিপিটি, গুগল ক্লাউড এবং পাইথনের সাথে একটি ডকুমেন্ট বিশ্লেষক তৈরি করুন

Erstellen Sie einen Dokumentenanalysator mit ChatGPT, Google Cloud und Python

Cree un analizador de documentos con ChatGPT, Google Cloud y Python

Créer un analyseur de documents avec ChatGPT, Google Cloud et Python

चैटजीपीटी, गूगल क्लाउड और पायथन के साथ एक दस्तावेज़ विश्लेषक बनाएं

ChatGPT、Google Cloud、Python を使用してドキュメント アナライザーを構築する

ChatGPT, Google Cloud, Python을 사용하여 문서 분석기 구축

Crie um analisador de documentos com ChatGPT, Google Cloud e Python

ChatGPT, Google Cloud ve Python ile Doküman Analizörü Oluşturun

Xây dựng Trình phân tích tài liệu với ChatGPT, Google Cloud và Python

使用 ChatGPT、Google Cloud 和 Python 构建文档分析器

Wiseman categorized bikers into two main groups: those who wear protective gear and those who eventually experience accidents. Similarly, companies can be divided into two groups regarding data protection: those who learn from the experiences of others and take precautions and those who disregard the risks posed by hardware failures, site crashes, malware, viruses, software malfunctions, and human errors. Some individuals take measures to prevent data loss because they have heard stories from those who couldn't recover from such incidents (approximately 80% of those who have experienced data loss). Additionally, larger companies face even fewer chances of successfully dealing with data loss. Data security and protection are paramount for organizations worldwide in the modern business landscape. Numerous threats can result in data corruption, destruction, or loss. While it may be challenging to anticipate these factors, preventive measures can still be taken. This is where Backup and Disaster Recovery (DR) options come into play, ensuring the resilience and recoverability of data even in the most unexpected circumstances. In this article, we will explore the Backup and DR and explore establishing a robust infrastructure that enhances data security and protection in the face of malicious activities. Backup and Disaster Recovery Definition Let's begin by understanding the concepts of Backup and DR. What is data backup? Backup involves creating copies or archives of critical data and storing them in an alternate location, allowing for recovery in the event of data loss or corruption. Backup options in and on-premises infrastructure should possess the following characteristics: cloud services Centralized backup management across various workloads. Efficient utilization of storage to minimize costs. Minimal recovery times. On the other hand, (DR) refers to the processes and measures implemented to ensure the continuity of operations and the restoration of vital infrastructure following an adverse event. disaster recovery When discussing Backup and DR, two essential metrics come into play when assessing the likelihood of recovering from natural or other disasters: : This is the maximum acceptable downtime for applications as per your service level agreement (SLA). Recovery Time Objective (RTO) : This indicates your application's maximum acceptable data loss duration due to malicious activities. Recovery Point Objective (RPO) In September 2022, Google introduced the Google Cloud Backup and DR service, which enables centralized management of Backup and DR directly from the Google Cloud console. Now, let's delve into the key features and Google Cloud benefits for workload Backup and restoration. Google Cloud Server Backup Google Cloud offers its users a straightforward approach to setting up, restoring, and managing disaster recovery (DR) and various cloud backup strategies. A key focus of the GCP backup solution is to provide a centralized management experience. What does this mean? It means you can reduce the time spent on management and control by having a comprehensive view of your data protection. With this capability, IT administrators can create application- and crash-consistent backups for virtual machines (VMs) on Compute Engine, VMware Engine, or on-premises VMware, as well as databases and file systems, all from a single interface. Your data will be stored in its original format, ensuring its readability for applications. By leveraging data protection in the cloud, you can store all your valuable information in one backup storage, in a cost-effective format, without extensive translation or transition efforts. This reduces the time required for recovery and allows critical business operations to be restored more quickly. To minimize the total cost of owning a backup solution, Google has developed a space-efficient technology called "incremental forever" storage as part of Cloud Backup and Disaster Recovery. This solution follows the principle of paying only for what you use and need. Here's how it works: once you have your initial backup, Google Cloud will store subsequent backups by capturing only the block-level changes since the last backup session. This approach enables faster backups, reduces the impact on production resources, minimizes network bandwidth requirements, and lowers storage costs by optimizing the amount of storage consumed by your backups. Additionally, Google Cloud offers flexibility in terms of cost and data retention. With Google Cloud Storage, a managed service for storing unstructured data, you can choose the storage class that best suits your needs, balancing business requirements and cost efficiency. For example, Archive Storage provides the lowest-cost option for highly durable storage, suitable for data archiving, with low-latency access, online backup, and disaster recovery capabilities, ensuring that your data is available within milliseconds. Key Benefits of the Service Many people mistakenly assume that storing data in the cloud automatically guarantees its safety in the face of disasters or threats like malware. However, it's important to remember that simply using the cloud does not guarantee sufficient data security. It's crucial to assess what backup and disaster recovery capabilities your cloud provider offers. Here are the benefits of Google Cloud Platform (GCP) backup and disaster recovery: GCP provides centralized data management, allowing you to set up, monitor, and manage multiple workloads from a single dashboard. Streamlined data management and restoration: : Google Cloud Backup and DR services are VMware and application-aware, supporting many databases such as MS SQL, PostgreSQL, SAP DB, Oracle, and MongoDB. This capability enables you to significantly reduce your RPO, potentially to as low as 10 minutes, without overloading your systems due to the always incremental concept. Minimal Recovery Point Objective (RPO) : With GCP, you can instantly mount your backup data to hosts, avoiding waiting for lengthy database restoration processes. This feature ensures that you can get back to business immediately. Additionally, if you are on-premises, you can leverage the onsite cache to restore your VMware and mount the data locally, resulting in lightning-fast recovery. Moreover, you can access any 10-minute data slice since you started using the service. Instant recovery to eliminate downtime : GCP allows you to instantly mount any slice of data as a writable snapshot, facilitating activities such as analysis, audit, legal processes, or testing. Snapshot functionality for various purposes : Google Cloud Backup and DR Service charge you based on monthly usage, enabling you to pay only for what you use. The pricing is competitive, starting as low as $0.03 per GiB per month for regular data and varying for different types of databases. Additionally, Cloud Storage offers affordable space. No prior reservations are required; you only pay for your actual data. Cost optimization : GCP's backup solution leverages incremental changes added to your initial backup, reducing the need for duplicating data. This approach saves both time and costs. Efficient storage utilization : GCP allows you to restore your infrastructure with a single click. You can script complete data restoration to another region or migrate your on-premises infrastructure to the cloud within minutes, all without incurring additional costs. This feature saves you money while ensuring your data is highly protected. Additionally, you can schedule backup and DR testing, paying only for the target infrastructure when needed to restore and verify data and application integrity. Simplified infrastructure restoration In summary, GCP Backup and DR solutions offer unique features that protect your cloud workloads from unforeseen events. They also provide a secure backup solution for on-premises data by storing it in Google Cloud. By leveraging , you can optimize costs and benefit from efficient disaster recovery options in the cloud. these capabilities Compared to traditional legacy software and new entrants in the market, GCP's Backup and DR functionalities stand out as a comprehensive, unique, and reliable solution.

Essential Insights on Google Cloud Backup and Disaster Recovery Service

Información esencial sobre el servicio de copia de seguridad y recuperación ante desastres de Google Cloud

Informations essentielles sur le service de sauvegarde et de reprise après sinistre de Google Cloud

Google क्लाउड बैकअप और डिजास्टर रिकवरी सर्विस पर आवश्यक अंतर्दृष्टि

Google Cloud のバックアップおよび障害復旧サービスに関する重要な洞察

Informações essenciais sobre o serviço de backup e recuperação de desastres do Google Cloud

Thông tin chi tiết cần thiết về Dịch vụ khôi phục sau thảm họa và sao lưu đám mây của Google

Google Cloud 备份和灾难恢复服务的基本见解

Motivation and Introduction Build previews aren't anything new or innovative. Heroku had them for years. If you're building on Google Cloud Platform however, things are a little more difficult. Google Cloud Build can be used to deploy to Cloud Functions, Cloud Run, Kubernetes, App Engine etc. etc. This is what makes previews slightly complicated, as Cloud Build doesn't know where you're deploying your application or service to. They have a GitHub App to connect the repositories and show the build status in GitHub, but that's it. No previews, comments or any of the "normal" stuff we're used to from products like Heroku, Vercel or Fly. There's an "easy" way to post a comment on a GitHub Pull Request from Cloud Build with a simple HTTP request. But why easy when you can use the . GitHub Deployments API With this API, you can create Environments, have different variables per environment and show build & deployment status directly on a Pull Request. And once the deployment is done on your CD platform, you can add the URL to the deployment status. This is what we're going to do in this article. GitHub Deployment Status Again, there's another way of doing this by using the Deployments API directly with HTTP requests. This can be tricky sometimes though and you'll need to define a Personal Access Token to authenticate with GitHub. Instead, we decided to put together a little Node.js service that uses GitHub Apps to generate an app token and interact with the Deployments API. This allows a little finer control over the deployment process. Disclaimer This article is exclusively written for Google Cloud Platform and GitHub. Basic understanding of Google Cloud Platform, Google Cloud Build and IAM permissions is assumed. You should have a Google Cloud Platform project set up. GitHub Deployer (Node.js & Docker) GitHub Deployer is a tiny Node.js service that authorized with a GitHub App and interacts with the GitHub Deployments API. Commands are predefined and some information needs to be built in to the Docker image, as otherwise the runtime configuration in Cloud Build would get too complex. The Docker image is not ready built; you'll need to build it yourself and push it to your registry. The best place is Google Artifact Registry where the images for your application are stored as well (Container Registry is deprecated): If not done already, create an Artifact Registry repository Create a GitHub App and install it on your organization Create a GitHub App Required permissions: 'pull_requests', 'deployments', 'metadata' Copy the App ID (from the App Settings screen), and the App Installation ID (after clicking "Configure" you'll find the installation ID in the URL. No idea where else that can be found) Create & download a private key and convert it to base64 ( ) base64 -i your.private-key.pem Build the Docker image Source Cloud Build runs on , so we need to build the image for the x86 platform: amd64 buildx docker buildx build --platform linux/amd64 . -t us-central1-docker.pkg.dev/[PROJECT]/docker/gh-deployer \ --build-arg GH_APP_ID=[GITHUB_APP_ID] \ --build-arg GH_APP_PRIVATE_KEY=[GITHUB_PRIVATE_KEY_BASE_64] \ --build-arg GH_APP_INSTALLATION_ID=[GITHUB_INSTALLATION_ID] \ --build-arg GH_OWNER=[GITHUB_ORG] Push the image to your registry: docker push us-central1-docker.pkg.dev/[PROJECT]/docker/gh-deployer Run the Docker image locally docker run -it -e REPO_NAME=test -e REF=main -e ENVIRONMENT=test us-central1-docker.pkg.dev/[PROJECT]/docker/gh-deployer:latest create Required environment variables: : the name of the repository (e.g. ) REPO_NAME required gh-deployer : the branch, tag or SHA to deploy (e.g. ) REF required main : the environment to deploy to (e.g. ) ENVIRONMENT required preview : if set to , the deployment will be deleted after a certain time TRANSIENT_ENVIRONMENT optional/false true : a description of the deployment DESCRIPTION optional The following commands are available as first argument: - create a new deployment create - the build is pending pending - the build is in progress in_progress - the build is queued queued - the deployment was successful success - something went wrong error - the deployment failed failure With Cloud Build the options are currently a bit limited. There's no state as Cloud Build needs to start in order to create the initial deployment. doesn't make much sense either, so in our own setup, we're using the following: pending queued to create the transient deployment for a commit gh-deployer/create directly after create pgh-deployer/ending Run kaniko build to build the Docker image for deployment after the build is done and before the image is deployed gh-deployer/in_progress after the image is deployed gh-deployer/success Again, with Cloud Build we don't know where the deployment is going to be, so there are two ways to pass the deployment URL to the step: success In a build step, write the URL into /workspace/deployer_environment_url Pass a second argument to the Docker image after success We use Cloud Run for our deployments, so here's the build step to retrieve the deployment URL for a given Pull Request number: - name: gcr.io/google.com/cloudsdktool/cloud-sdk env:

  - PR_NUMBER=$_PR_NUMBER

script: >-

  gcloud run services list --project [project] --filter preview-$PR_NUMBER

  --format "value(status.address.url)" > /workspace/deployer_environment_url Cloud Build Configuration / Terraform cloudbuild.yaml cloudbuild.yaml Here's a complete exmaple using the configuration file. We use Kaniko to build and Cloud Run as deplyoment target. cloudbuild.yaml If you work with Terraform, there's a Terraform file afailable as well: preview.tf GitHub Actions By using the Deployments API, you can trigger GitHub Actions on . This makes it easy to run quality assurance checks, end-to-end tests etc. on each preview build. Here's an example for running on each preview using the that is passed in for the state: deployment_status Playwright environment_url success playwright-qa: if: ${{ github.event.deployment\_status.state == 'success' }}

timeout-minutes: 60

runs-on: ubuntu\-latest

steps:

  \- uses: actions/[\[email protected\]](/cdn-cgi/l/email-protection)

  \- uses: actions/setup\-[\[email protected\]](/cdn-cgi/l/email-protection)

    with:

      node-version: 20

      cache: 'npm'

  \- run: npm ci

  \- name: Install Playwright Browsers

    run: npm exec playwright install \-\-with\-deps \-y

  \- name: Run Playwright tests

    run: pnpm exec playwright test

    env:

      BASE\_URL: ${{github.event.deployment\_status.environment\_url}}

  \- uses: actions/upload\-[\[email protected\]](/cdn-cgi/l/email-protection)

    if: always()

    with:

      name: playwright\-report

      path: playwright\-report/

      retention-days: 30 Optimizations the basic image is relatively large. You can to speed up the builds. gcr.io/google.com/cloudsdktool/cloud-sdk build your own small gcloud image with Cloud Run components Conclusion GitHub Deployment Status Done Comments on GitHub Pull Requests are easy at first, but when you have multiple deployments per Pull Request, failing builds etc. they don't offer a lot of flexibility and are hard to maintain / update. The GitHub Deployments API offers an elegant way to create and manage deployments from any third party CI/CD system. With the we tried to streamline the interaction with the deployments API and take care of some side effects or pitfalls that are impossible to solve with just the HTTP API. GitHub Deployer Another benefit of using the GitHub Deployments API is that you can use the trigger for GitHub Actions and get the directly with the event payload. deployment_status environment_url You can find a little more detailled installation / build instructions and all the code on the GitHub repository , please / or . If you have any questions or comments reach out on BlueSky Twitter join the discussion on GitHub Also published here.

GOOGLE CLOUD

cloud.google.com

10K+ employees

Since 1998

COMPANY RANKING

GOOGLE CLOUD

EVERGREEN INDEX #502

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Google Cloud WIKI

GOOGLE CLOUD

cloud.google.com

10K+ employees

Since 1998

COMPANY RANKING

GOOGLE CLOUD

EVERGREEN INDEX #502

HACKERNOON STORIES ON GOOGLE CLOUD

GOOGLE CLOUD NEWS & MENTIONS

Representing this company? Click here to claim and customize this page!

Google Cloud WIKI