Search the world&#x27;s information, including webpages, images, videos and more. Google has many special features to help you find exactly what you&#x27;re looking for.

Cloud Computing Services | Google Cloud

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 ...

Google Cloud Platform - Wikipedia

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 ...

Google Cloud | LinkedIn

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

Google Cloud Platform Company Profile: Valuation & Investors | PitchBook

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

Experience Google Cloud Next ’24

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

Google Cloud - Crunchbase Company Profile & Funding

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

What is Google Cloud?

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.

Google Cloud Development Services | GCP Technologies by DataArt

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.

Top Google Cloud Companies 2024 | Built In

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 ...

What Is Google Cloud Platform?

We cannot provide a description for this page right now

Onix - Google Cloud

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.

Google Cloud Platform: What it is, how to use it, and ... - Acronis

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

Customers | Google Cloud | Google Cloud

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

Google Cloud

How Can AI and Blockchain Change the Way We Compute? Have you ever asked how data processing might shift from a single system to a coordinated network of specialized units? A group of developers is set to explore this possibility. Flare, in collaboration with Google Cloud, is hosting a hackathon at UC Berkeley to test a method where heavy AI tasks are processed offchain and their results are recorded onchain. The Story Behind the Event Picture a system that takes compute-heavy tasks away from the blockchain. Instead of overloading the blockchain with intense operations, these tasks are moved to a secure environment known as a Trusted Execution Environment (TEE). Once the tasks are completed in the TEE, the outcomes are logged on the blockchain through cryptographic proofs. This method addresses a key challenge: the computational limits of onchain processing. By separating the tasks, the system aims to maintain the blockchain's record of events while using dedicated hardware to handle the heavy lifting. This approach is the central focus of the hackathon. Flare announced an AI hackathon at UC Berkeley in collaboration with Google Cloud. The event runs from March 7 to 9 at California Memorial Stadium. Participants will combine offchain compute methods with blockchain verification protocols using Trusted Execution Environments (TEEs). Event Details Prize Pool: $100,000 total

In-Person: $60,000
Virtual: $40,000


Tracks:


Social AI Agents


RAG Knowledge Systems


DeFAI


Consensus Learning Developers will work with tools from Google Cloud’s Confidential Computing and Flare’s blockchain data protocols. The goal is to shift intensive compute tasks offchain and link the results onchain with cryptographic proofs. Technical Approach The hackathon focuses on using TEEs to process AI tasks outside the blockchain. Compute tasks will run offchain and the outcomes will be recorded onchain with cryptographic proofs. This method addresses the limits of onchain processing and enables new techniques for handling data and computation in blockchain environments. Industry Collaboration Flare works with Blockchain at Berkeley and Google Cloud for this event. Blockchain at Berkeley will engage blockchain developers, students, and related groups. Google Cloud supplies time series data to Flare’s Time Series Oracle, a role it has maintained since January 2024. Format and Participation The event takes place in two formats: On Campus: At California Memorial Stadium, UC Berkeley.
Online: Hosted on DoraHacks. Registration opens in early February. Teams are invited to develop systems that integrate offchain AI compute with onchain verification. Looking Ahead Flare plans further work in offchain compute and blockchain integration as part of its 2025 roadmap. The hackathon offers a platform to test methods that process data offchain and verify results onchain. Teams will create applications that combine offchain compute with blockchain records. Interested participants may apply at the event website. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR How Can AI and Blockchain Change the Way We Compute? Have you ever asked how data processing might shift from a single system to a coordinated network of specialized units? A group of developers is set to explore this possibility. Flare , in collaboration with Google Cloud, is hosting a hackathon at UC Berkeley to test a method where heavy AI tasks are processed offchain and their results are recorded onchain. Flare The Story Behind the Event Picture a system that takes compute-heavy tasks away from the blockchain. Instead of overloading the blockchain with intense operations, these tasks are moved to a secure environment known as a Trusted Execution Environment (TEE). Once the tasks are completed in the TEE, the outcomes are logged on the blockchain through cryptographic proofs. This method addresses a key challenge: the computational limits of onchain processing. By separating the tasks, the system aims to maintain the blockchain's record of events while using dedicated hardware to handle the heavy lifting. This approach is the central focus of the hackathon. Flare announced an AI hackathon at UC Berkeley in collaboration with Google Cloud. The event runs from March 7 to 9 at California Memorial Stadium. Participants will combine offchain compute methods with blockchain verification protocols using Trusted Execution Environments (TEEs). Event Details Prize Pool: $100,000 total

In-Person: $60,000
Virtual: $40,000 Tracks:


Social AI Agents


RAG Knowledge Systems


DeFAI


Consensus Learning Prize Pool: $100,000 total In-Person: $60,000
Virtual: $40,000 Prize Pool: In-Person: $60,000 Virtual: $40,000 In-Person: $60,000 Virtual: $40,000 Tracks: Social AI Agents


RAG Knowledge Systems


DeFAI


Consensus Learning Tracks: Social AI Agents RAG Knowledge Systems DeFAI Consensus Learning Social AI Agents Social AI Agents RAG Knowledge Systems RAG Knowledge Systems DeFAI DeFAI Consensus Learning Consensus Learning Developers will work with tools from Google Cloud’s Confidential Computing and Flare’s blockchain data protocols. The goal is to shift intensive compute tasks offchain and link the results onchain with cryptographic proofs. Technical Approach The hackathon focuses on using TEEs to process AI tasks outside the blockchain. Compute tasks will run offchain and the outcomes will be recorded onchain with cryptographic proofs. This method addresses the limits of onchain processing and enables new techniques for handling data and computation in blockchain environments. Industry Collaboration Flare works with Blockchain at Berkeley and Google Cloud for this event. Blockchain at Berkeley will engage blockchain developers, students, and related groups. Google Cloud supplies time series data to Flare’s Time Series Oracle, a role it has maintained since January 2024. Format and Participation The event takes place in two formats: On Campus: At California Memorial Stadium, UC Berkeley. Online: Hosted on DoraHacks. On Campus: At California Memorial Stadium, UC Berkeley. On Campus: Online: Hosted on DoraHacks. Online: Registration opens in early February. Teams are invited to develop systems that integrate offchain AI compute with onchain verification. Looking Ahead Flare plans further work in offchain compute and blockchain integration as part of its 2025 roadmap. The hackathon offers a platform to test methods that process data offchain and verify results onchain. Teams will create applications that combine offchain compute with blockchain records. Interested participants may apply at the event website. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

How Flare is Unlocking Offchain AI: Flare and Google Cloud at UC Berkeley for Web3 & AI Hackathon

Hoe Flare Offchain AI ontsluit: Flare en Google Cloud by UC Berkeley vir Web3 & AI Hackathon

ፍላይ እንዴት Offchain AI: Flare እና Google Cloud በUC Berkeley ለWeb3 እና AI Hackathon እየከፈተ ነው

Com Flare desbloqueja Offchain AI: Flare i Google Cloud a UC Berkeley per a Web3 i AI Hackathon

Cómo Flare está desbloqueando la IA fuera de la cadena: Flare y Google Cloud en la UC Berkeley para el hackathon Web3 & AI

Comment Flare débloque l'IA hors chaîne : Flare et Google Cloud à l'UC Berkeley pour le Web3 & AI Hackathon

Flare がオフチェーン AI を解き放つ方法: Flare と Google Cloud が UC Berkeley で Web3 & AI ハッカソンを開催

Ndenge nini Flare ezali ko débloquer Offchain AI: Flare na Google Cloud na UC Berkeley pona Web3 & AI Hackathon

Jak Flare odblokowuje sztuczną inteligencję poza łańcuchem: Flare i Google Cloud na UC Berkeley na Hackathonie Web3 i AI

Si po zhbllokon Flare Offchain AI: Flare dhe Google Cloud në UC Berkeley për Web3 & AI Hackathon

Flare Offchain AI-ni nädip açýar: Web3 & AI Hackathon üçin UC Berkeley-de Flare we Google Cloud

Як Flare розблоковує Offchain AI: Flare і Google Cloud в UC Berkeley для Web3 & AI Hackathon

Indlela i-Flare evula ngayo i-Offchain AI: I-Flare kunye neLifu likaGoogle e-UC Berkeley yeWeb3 kunye ne-AI Hackathon

The Google Cloud Task Workflow Is a Great Way to Schedule Cloud Functions

Building an API with Go, PostgreSQL, Google Cloud and CockroachDB

Flare has launched its Blockchain Machine Images on Google Cloud Marketplace, enabling developers to deploy nodes across more than 20 blockchain networks with unprecedented speed and efficiency. The integration, announced on October 25, 2024, addresses a persistent challenge in blockchain development - the time-consuming process of node deployment and maintenance. Through this solution, developers can now launch full blockchain nodes within minutes, a process that traditionally required hours or days of setup and synchronization. "This integration removes barriers for builders, making node deployment accessible to anyone with just a few clicks," states Josh Edwards, Flare's VP of Engineering. The move positions Flare at the forefront of blockchain infrastructure development, expanding beyond the standard Bitcoin and Ethereum offerings common in the market. The platform's capabilities extend across multiple dimensions: Instant Deployment: Developers can launch nodes for Flare, Bitcoin, Ethereum, and other networks through a streamlined interface


Cost Reduction: The solution eliminates the need for extensive infrastructure investment


Maintenance Automation: Updates and patches can be implemented with single-click operations


Unlimited RPC Calls: The system supports unrestricted blockchain data access for intensive workloads For enterprises considering blockchain adoption, this development reduces the technical barriers to entry. Companies can now initiate blockchain pilots without investing in specialized infrastructure or technical expertise. The platform's integration with Google Cloud's infrastructure provides the security and scalability requirements that enterprise users demand. Dai Vu, Managing Director of Marketplace & ISV GTM Programs at Google Cloud, emphasizes the significance: "This integration enables customers to manage blockchains on Google Cloud's global infrastructure, supporting their digital transformation initiatives." The launch builds upon Flare's existing relationship with Google Cloud, complementing their API Portal that facilitates blockchain transactions and state access. This expansion demonstrates Flare's commitment to creating a comprehensive blockchain data infrastructure. Market implications suggest this development could accelerate blockchain adoption across industries. Financial technology companies can now integrate blockchain data into their applications with reduced technical overhead. The platform's support for data indexing and historical data access opens possibilities for advanced analytics and machine learning applications in the blockchain space. The broader impact extends to the blockchain ecosystem as a whole. By simplifying node deployment across multiple networks, Flare's solution could foster increased decentralization and network resilience. The ability to rapidly deploy nodes may lead to more robust and distributed blockchain networks. Looking ahead, Flare plans to expand support to additional blockchain networks, potentially establishing a new standard for blockchain infrastructure deployment. The company's focus on data-intensive use cases, including machine learning and real-world asset tokenization, positions this development as a stepping stone toward more sophisticated blockchain applications. This integration represents a convergence of enterprise-grade cloud infrastructure with blockchain technology, potentially catalyzing the next wave of blockchain innovation and adoption. As organizations continue to explore blockchain applications, solutions that simplify technical implementation while maintaining security and scalability will prove crucial for mainstream adoption. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Flare has launched its Blockchain Machine Images on Google Cloud Marketplace, enabling developers to deploy nodes across more than 20 blockchain networks with unprecedented speed and efficiency. Flare The integration, announced on October 25, 2024, addresses a persistent challenge in blockchain development - the time-consuming process of node deployment and maintenance. Through this solution, developers can now launch full blockchain nodes within minutes, a process that traditionally required hours or days of setup and synchronization. "This integration removes barriers for builders, making node deployment accessible to anyone with just a few clicks," states Josh Edwards, Flare's VP of Engineering. The move positions Flare at the forefront of blockchain infrastructure development, expanding beyond the standard Bitcoin and Ethereum offerings common in the market. The platform's capabilities extend across multiple dimensions: Instant Deployment: Developers can launch nodes for Flare, Bitcoin, Ethereum, and other networks through a streamlined interface Cost Reduction: The solution eliminates the need for extensive infrastructure investment Maintenance Automation: Updates and patches can be implemented with single-click operations Unlimited RPC Calls: The system supports unrestricted blockchain data access for intensive workloads Instant Deployment: Developers can launch nodes for Flare, Bitcoin, Ethereum, and other networks through a streamlined interface Instant Deployment: Developers can launch nodes for Flare, Bitcoin, Ethereum, and other networks through a streamlined interface Cost Reduction: The solution eliminates the need for extensive infrastructure investment Cost Reduction: The solution eliminates the need for extensive infrastructure investment Maintenance Automation: Updates and patches can be implemented with single-click operations Maintenance Automation: Updates and patches can be implemented with single-click operations Unlimited RPC Calls: The system supports unrestricted blockchain data access for intensive workloads Unlimited RPC Calls: The system supports unrestricted blockchain data access for intensive workloads For enterprises considering blockchain adoption, this development reduces the technical barriers to entry. Companies can now initiate blockchain pilots without investing in specialized infrastructure or technical expertise. The platform's integration with Google Cloud's infrastructure provides the security and scalability requirements that enterprise users demand. Dai Vu, Managing Director of Marketplace & ISV GTM Programs at Google Cloud, emphasizes the significance: "This integration enables customers to manage blockchains on Google Cloud's global infrastructure, supporting their digital transformation initiatives." The launch builds upon Flare's existing relationship with Google Cloud, complementing their API Portal that facilitates blockchain transactions and state access. This expansion demonstrates Flare's commitment to creating a comprehensive blockchain data infrastructure. Market implications suggest this development could accelerate blockchain adoption across industries. Financial technology companies can now integrate blockchain data into their applications with reduced technical overhead. The platform's support for data indexing and historical data access opens possibilities for advanced analytics and machine learning applications in the blockchain space. The broader impact extends to the blockchain ecosystem as a whole. By simplifying node deployment across multiple networks, Flare's solution could foster increased decentralization and network resilience. The ability to rapidly deploy nodes may lead to more robust and distributed blockchain networks. Looking ahead, Flare plans to expand support to additional blockchain networks, potentially establishing a new standard for blockchain infrastructure deployment. The company's focus on data-intensive use cases, including machine learning and real-world asset tokenization, positions this development as a stepping stone toward more sophisticated blockchain applications. This integration represents a convergence of enterprise-grade cloud infrastructure with blockchain technology, potentially catalyzing the next wave of blockchain innovation and adoption. As organizations continue to explore blockchain applications, solutions that simplify technical implementation while maintaining security and scalability will prove crucial for mainstream adoption. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Flare Revolutionizes Blockchain Node Deployment with Google Cloud Marketplace Integration

Flare revolusioneer Blockchain Node-ontplooiing met Google Cloud Marketplace-integrasie

Flare revoluciona la implementación de nodos blockchain con la integración de Google Cloud Marketplace

Flare Merevolusi Penerapan Node Blockchain dengan Integrasi Google Cloud Marketplace

Flare が Google Cloud Marketplace との統合によりブロックチェーン ノードの展開を革新

Flare Google Cloud Marketplace интеграциясымен блокчейн түйінін орналастыруда төңкеріс жасайды

Flare Google Cloud Marketplace интеграциясы менен блокчейн түйүнүнүн жайылышын революция кылды

Flare Merevolusikan Penerapan Nod Blockchain dengan Penyepaduan Pasaran Awan Google

फ्लेयरले गुगल क्लाउड मार्केटप्लेस एकीकरणको साथ ब्लकचेन नोड परिनियोजनलाई क्रान्ति गर्दछ

Flare Google Cloud Marketplace Integration සමඟ Blockchain Node Deployment විප්ලවීය කරයි

கூகுள் கிளவுட் மார்க்கெட்ப்ளேஸ் ஒருங்கிணைப்புடன் பிளாக்செயின் நோட் வரிசைப்படுத்தலை ஃப்ளேர் புரட்சிகரமாக்குகிறது

“Flare” “Google Cloud Marketplace Integration” bilen “Blockchain” düwünleriniň ýerleşdirilmegini rewolýusiýa edýär

Flare cách mạng hóa việc triển khai nút Blockchain với tích hợp Google Cloud Marketplace

We are back with another Company of the Week feature! Every week, we share an awesome tech brand from our tech company database, making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. This week, we are proud to present Google Cloud! Alphabet’s Google Cloud offers customers computing resources to power their artificial intelligence and machine learning workloads, capitalizing on the company’s years of experience running and managing large (dare we say, humongous!) servers. Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon! Google Cloud <> HackerNoon Tech Community Google Cloud recently utilized HackerNoon for its content marketing needs, highlighting some exciting courses and features to HackerNoon readers plus an offer to utilize up to $350 in free credits to test, deploy, and explore Google Cloud for 90 days. You can check out some of their recent posts below: Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level
Axion Processors: Google's First Arm-based CPUs
Actionable Threat Intelligence at Google Scale: Meet Google Threat Intelligence Powered by Gemini
Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI
Meet New & Improved BigQuery: Single, Unified AI-Ready Data Platform Learn How You Can Book a Spot on HackerNoon’s Billboard Meet Google Cloud: #FunFacts We thought it would be interesting if we asked Gemini to tell us some fun little facts about Google Cloud. Here’s what it said: Did you know that Google Cloud powers the search engine giant's own services like Google Search, YouTube, and Gmail? That's right, the same technology that helps you find cat videos also runs some of the world's busiest websites! How about this: Google Cloud's enterprise data warehouse, BigQuery, is so powerful that it can process over 110 terabytes of data per second! That's like streaming thousands of high-definition movies simultaneously. 🤯 Google Cloud is helping to protect endangered species! Many organizations use Google Earth Engine, a cloud computing platform, to analyze satellite imagery and track deforestation, wildlife populations, and other environmental factors. It's like having a super-powered microscope for our planet! 🌍🐼 How’s that for interesting 😏 Experience the power of Google Cloud yourself and get $300 + $50 extra free trial credits Share Your Company's Story via HackerNoon That's all this week, folks! Stay Creative, Stay Iconic. The HackerNoon Team We are back with another Company of the Week feature! Every week, we share an awesome tech brand from our tech company database , making their evergreen mark on the internet. This unique HackerNoon database ranks S&P 500 companies and top startups of the year alike. Company of the Week Company of the Week tech company database tech company database This week, we are proud to present Google Cloud ! Alphabet’s Google Cloud offers customers computing resources to power their artificial intelligence and machine learning workloads, capitalizing on the company’s years of experience running and managing large (dare we say, humongous!) servers. Google Cloud Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon! Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon ! Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Google Cloud <> HackerNoon Tech Community Google Cloud recently utilized HackerNoon for its content marketing needs, highlighting some exciting courses and features to HackerNoon readers plus an offer to utilize up to $350 in free credits to test, deploy, and explore Google Cloud for 90 days. You can check out some of their recent posts below: exciting courses utilize Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level Axion Processors: Google's First Arm-based CPUs Actionable Threat Intelligence at Google Scale: Meet Google Threat Intelligence Powered by Gemini Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI Meet New & Improved BigQuery: Single, Unified AI-Ready Data Platform Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level Axion Processors: Google's First Arm-based CPUs Axion Processors: Google's First Arm-based CPUs Actionable Threat Intelligence at Google Scale: Meet Google Threat Intelligence Powered by Gemini Actionable Threat Intelligence at Google Scale: Meet Google Threat Intelligence Powered by Gemini Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI Google Cloud x Gemini: Accomplish More in the Cloud with Generative AI Meet New & Improved BigQuery: Single, Unified AI-Ready Data Platform Meet New & Improved BigQuery: Single, Unified AI-Ready Data Platform Learn How You Can Book a Spot on HackerNoon’s Billboard Learn How You Can Book a Spot on HackerNoon’s Billboard Learn How You Can Book a Spot on HackerNoon’s Billboard Learn How You Can Book a Spot on HackerNoon’s Billboard Learn How You Can Book a Spot on HackerNoon’s Billboard Meet Google Cloud: #FunFacts We thought it would be interesting if we asked Gemini to tell us some fun little facts about Google Cloud. Here’s what it said: Did you know that Google Cloud powers the search engine giant's own services like Google Search, YouTube, and Gmail? That's right, the same technology that helps you find cat videos also runs some of the world's busiest websites! Did you know that Google Cloud powers the search engine giant's own services like Google Search, YouTube, and Gmail? That's right, the same technology that helps you find cat videos also runs some of the world's busiest websites! Did you know that Google Cloud powers the search engine giant's own services like Google Search, YouTube, and Gmail? How about this: Google Cloud's enterprise data warehouse, BigQuery, is so powerful that it can process over 110 terabytes of data per second! That's like streaming thousands of high-definition movies simultaneously. 🤯 How about this: Google Cloud's enterprise data warehouse, BigQuery, is so powerful that it can process over 110 terabytes of data per second! That's like streaming thousands of high-definition movies simultaneously. 🤯 Google Cloud is helping to protect endangered species! Many organizations use Google Earth Engine, a cloud computing platform, to analyze satellite imagery and track deforestation, wildlife populations, and other environmental factors. It's like having a super-powered microscope for our planet! 🌍🐼 Google Cloud is helping to protect endangered species! Many organizations use Google Earth Engine, a cloud computing platform, to analyze satellite imagery and track deforestation, wildlife populations, and other environmental factors. It's like having a super-powered microscope for our planet! 🌍🐼 Google Cloud is helping to protect endangered species! How’s that for interesting 😏 Experience the power of Google Cloud yourself and get $300 + $50 extra free trial credits Experience the power of Google Cloud yourself and get $300 + $50 extra free trial credits $300 + $50 extra free trial credits Share Your Company's Story via HackerNoon Share Your Company's Story via HackerNoon Share Your Company's Story via HackerNoon Share Your Company's Story via HackerNoon That's all this week, folks! Stay Creative, Stay Iconic. The HackerNoon Team

Meet Google Cloud: HackerNoon Company of the Week

Google ক্লাউডের সাথে দেখা করুন: সপ্তাহের হ্যাকারনুন কোম্পানি

Lernen Sie Google Cloud kennen: HackerNoon-Unternehmen der Woche

Conozca Google Cloud: la empresa de la semana de HackerNoon

Découvrez Google Cloud : Entreprise de la semaine selon HackerNoon

गूगल क्लाउड से मिलिए: हैकरनून कंपनी ऑफ द वीक

Google Cloud のご紹介: HackerNoon の今週の企業

Google Cloud를 만나보세요: HackerNoon 주간 회사

Conheça o Google Cloud: HackerNoon Empresa da Semana

Встречайте Google Cloud: компания недели HackerNoon

Google Cloud ile tanışın: HackerNoon Haftanın Şirketi

Gặp gỡ Google Cloud: Công ty của tuần HackerNoon

了解 Google Cloud：HackerNoon 本周公司

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

GOOGLE CLOUD

cloud.google.com

10K+ employees

Since 2008

COMPANY RANKING

GOOGLE CLOUD

EVERGREEN INDEX #6009

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

Google Cloud WIKI

GOOGLE CLOUD

cloud.google.com

10K+ employees

Since 2008

COMPANY RANKING

GOOGLE CLOUD

EVERGREEN INDEX #6009

HACKERNOON STORIES ON GOOGLE CLOUD

GOOGLE CLOUD NEWS & MENTIONS

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

Google Cloud WIKI