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Google Compute Engine

Run large-scale workloads on virtual machines hosted on Google's infrastructure.
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What is Google Compute Engine?

Google Compute Engine is a service that provides virtual machines that run on Google infrastructure. Google Compute Engine offers scale, performance, and value that allows you to easily launch large compute clusters on Google's infrastructure. There are no upfront investments and you can run up to thousands of virtual CPUs on a system that has been designed from the ground up to be fast, and to offer strong consistency of performance.
Google Compute Engine is a tool in the Cloud Hosting category of a tech stack.

Who uses Google Compute Engine?

Companies
860 companies reportedly use Google Compute Engine in their tech stacks, including 9GAG, Snapchat, and CircleCI.

Developers
2382 developers on StackShare have stated that they use Google Compute Engine.

Google Compute Engine Integrations

New Relic, Kubernetes, Ansible, Twilio SendGrid, and Terraform are some of the popular tools that integrate with Google Compute Engine. Here's a list of all 47 tools that integrate with Google Compute Engine.

Why developers like Google Compute Engine?

Here鈥檚 a list of reasons why companies and developers use Google Compute Engine
Google Compute Engine Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Google Compute Engine in their tech stack.

Google Cloud IoT Core
Google Cloud IoT Core
Terraform
Terraform
Python
Python
Google Cloud Deployment Manager
Google Cloud Deployment Manager
Google Cloud Build
Google Cloud Build
Google Cloud Run
Google Cloud Run
Google Cloud Bigtable
Google Cloud Bigtable
Google BigQuery
Google BigQuery
Google Cloud Storage
Google Cloud Storage
Google Compute Engine
Google Compute Engine
GitHub
GitHub

Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

Check Out My Architecture: CLICK ME

Check out the GitHub repo attached

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Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer | 14 upvotes 85.3K views
atCodeFactorCodeFactor
Kubernetes
Kubernetes
CodeFactor.io
CodeFactor.io
Amazon EC2
Amazon EC2
Microsoft Azure
Microsoft Azure
Google Compute Engine
Google Compute Engine
Docker
Docker
AWS Lambda
AWS Lambda
Azure Functions
Azure Functions
Google Cloud Functions
Google Cloud Functions
#SAAS
#IAAS
#Containerization
#Autoscale
#Startup
#Automation
#Machinelearning
#AI
#Devops

CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

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Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner | 12 upvotes 362.9K views
atFundsCornerFundsCorner
Amazon SQS
Amazon SQS
Python
Python
Flask
Flask
MongoDB
MongoDB
Swagger UI
Swagger UI
Vue.js
Vue.js
Vuetify
Vuetify
vuex
vuex
Zappa
Zappa
AWS Lambda
AWS Lambda
Netlify
Netlify
Google Compute Engine
Google Compute Engine
Slack
Slack
GitLab CI
GitLab CI
Sentry
Sentry

At FundsCorner, we are on a mission to enable fast accessible credit to India鈥檚 Kirana Stores. We are an early stage startup with an ultra small Engineering team. All the tech decisions we have made until now are based on our core philosophy: "Build usable products fast".

Based on the above fundamentals, we chose Python as our base language for all our APIs and micro-services. It is ultra easy to start with, yet provides great libraries even for the most complex of use cases. Our entire backend stack runs on Python and we cannot be more happy with it! If you are looking to deploy your API as server-less, Python provides one of the least cold start times.

We build our APIs with Flask. For backend database, our natural choice was MongoDB. It frees up our time from complex database specifications - we instead use our time in doing sensible data modelling & once we finalize the data model, we integrate it into Flask using Swagger UI. Mongo supports complex queries to cull out difficult data through aggregation framework & we have even built an internal framework called "Poetry", for aggregation queries.

Our web apps are built on Vue.js , Vuetify and vuex. Initially we debated a lot around choosing Vue.js or React , but finally settled with Vue.js, mainly because of the ease of use, fast development cycles & awesome set of libraries and utilities backing Vue.

You simply cannot go wrong with Vue.js . Great documentation, the library is ultra compact & is blazing fast. Choosing Vue.js was one of the critical decisions made, which enabled us to launch our web app in under a month (which otherwise would have taken 3 months easily). For those folks who are looking for big names, Adobe, and Alibaba and Gitlab are using Vue.

By choosing Vuetify, we saved thousands of person hours in designing the CSS files. Vuetify contains all key material components for designing a smooth User experience & it just works! It's an awesome framework. All of us at FundsCorner are now lifelong fanboys of Vue.js and Vuetify.

On the infrastructure side, all our API services and backend services are deployed as server less micro-services through Zappa. Zappa makes your life super easy by packaging everything that is required to deploy your code as AWS Lambda. We are now addicted to the single - click deploys / updates through Zappa. Try it out & you will convert!

Also, if you are using Zappa, you can greatly simplify your CI / CD pipelines. Do try it! It's just awesome! and... you will be astonished by the savings you have made on AWS bills at end of the month.

Our CI / CD pipelines are built using GitLab CI. The documentation is very good & it enables you to go from from concept to production in minimal time frame.

We use Sentry for all crash reporting and resolution. Pro tip, they do have handlers for AWS Lambda , which made our integration super easy.

All our micro-services including APIs are event-driven. Our background micro-services are message oriented & we use Amazon SQS as our message pipe. We have our own in-house workflow manager to orchestrate across micro - services.

We host our static websites on Netlify. One of the cool things about Netlify is the automated CI / CD on git push. You just do a git push to deploy! Again, it is super simple to use and it just works. We were dogmatic about going server less even on static web sites & you can go server less on Netlify in a few minutes. It's just a few clicks away.

We use Google Compute Engine, especially Google Vision for our AI experiments.

For Ops automation, we use Slack. Slack provides a super-rich API (through Slack App) through which you can weave magical automation on boring ops tasks.

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艁ukasz Korecki
艁ukasz Korecki
CTO & Co-founder at EnjoyHQ | 6 upvotes 75.8K views
atEnjoyHQEnjoyHQ
collectd
collectd
Google Compute Engine
Google Compute Engine
StatsD
StatsD
Clojure
Clojure
Stackdriver
Stackdriver

We use collectd because of it's low footprint and great capabilities. We use it to monitor our Google Compute Engine machines. More interestingly we setup collectd as StatsD replacement - all our Clojure services push application-level metrics using our own metrics library and collectd pushes them to Stackdriver

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fontumi
fontumi
Firebase
Firebase
Node.js
Node.js
FeathersJS
FeathersJS
Vue.js
Vue.js
Google Compute Engine
Google Compute Engine
Dialogflow
Dialogflow
Cloud Firestore
Cloud Firestore
Git
Git
GitHub
GitHub
Visual Studio Code
Visual Studio Code

Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.

Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.

Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.

Git + GitHub + Visual Studio Code is a killer stack.

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Marcel Kornegoor
Marcel Kornegoor
CTO at AT Computing | 5 upvotes 134.4K views
atAT ComputingAT Computing
Linux
Linux
Ubuntu
Ubuntu
CentOS
CentOS
Debian
Debian
Red Hat Enterprise Linux
Red Hat Enterprise Linux
Fedora
Fedora
Visual Studio Code
Visual Studio Code
Jenkins
Jenkins
VirtualBox
VirtualBox
GitHub
GitHub
Docker
Docker
Kubernetes
Kubernetes
Google Compute Engine
Google Compute Engine
Ansible
Ansible
Puppet Labs
Puppet Labs
Chef
Chef
Python
Python
#ATComputing

Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

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Google Compute Engine's Features

  • High-performance virtual machines- Compute Engine鈥檚 Linux VMs are consistently performant, scalable, highly secure and reliable. Supported distros include Debian and CentOS. You can choose from micro-VMs to large instances.
  • Powered by Google鈥檚 global network- Create large compute clusters that benefit from strong and consistent cross-machine bandwidth. Connect to machines in other data centers and to other Google services using Google鈥檚 private global fiber network.
  • (Really) Pay for what you use- Google bills in minute-level increments (with a 10-minute minimum charge), so you don鈥檛 pay for unused computing time.
  • Load balancing- Native load-balancing technology helps you spread incoming network traffic across a pool of instances, so you can achieve maximum performance, throughput and availability at low cost.
  • Fast and easy provisioning- Quickly deploy large clusters of virtual machines with intuitive tools including a RESTful API, command-line interface and web-based Console. You can also use tools such as RightScale and Scalr to automatically manage your deployment.
  • Compliance and security- All data written to disk in Compute Engine is encrypted at rest using the AES-128-CBC algorithm. Compute Engine has completed ISO 27001, SSAE-16, SOC 1, SOC 2, and SOC 3 certifications, demonstrating our commitment to information security.

Google Compute Engine Alternatives & Comparisons

What are some alternatives to Google Compute Engine?
Google App Engine
Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
DigitalOcean
We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel.
Google Cloud Platform
It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube.
Amazon EC2
It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
Microsoft Azure
Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment.
See all alternatives

Google Compute Engine's Followers
2190 developers follow Google Compute Engine to keep up with related blogs and decisions.
Romain Xie
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Rahul Kumar
Ayush Singh
Ippei Ogiwara
Ravi Jain
Daniel Castro
Micha艂 Pierzchlewicz
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