Google Cloud Platform Blog

How to authenticate users on Google App Engine using Firebase

Thursday, October 6, 2016

Posted by Tristan Vanech, Technical Writer Intern Google App EnginevarietyFirebase Authenticationbuilt-in methodsolution for Firebase Authentication on App EngineGoogle Cloud Datastore

(click to enlarge)

How to use Docker to run ASP.NET Core apps on Google App Engine

Wednesday, October 5, 2016

Posted by Ivan Naranjo, Software DeveloperGoogle App EngineApp Engine Flexible Environment Step 1: Run your ASP.NET Core app locallyNuGet packages for Google Cloud APIs in .NETGoogle Cloud StorageGoogle Cloud Pub/Submachine learningdocumentation.NET Core runtimeBower

Step 2: Package it as a Docker container⸺

dotnet publish -c Release.NET Core Portable Appcd bin\Release\netcoreapp1.0\publish dotnet DemoFlexApp.dll

The Dockerfile that describes how to package the app files into a Docker container

The app.yaml file that tells the Google Cloud SDK tools how to deploy the app

FROM microsoft/dotnet:1.0.1-core COPY . /app WORKDIR /app EXPOSE 8080 ENV ASPNETCORE_URLS=http://*:8080 ENTRYPOINT ["dotnet", "DemoFlexApp.dll"]DockerfileMicrosoft official image⸺App Engine Flexible requirementsASPNETCORE_URLSruntime: custom vm: true Step 3: Deploy to App Engine Flexiblethese stepsGoogle Cloud Platform projectgcloud app deploy app.yamlgcloud app browse

Powering geospatial analysis: public geo datasets now on Google Cloud

Tuesday, October 4, 2016

Posted Peter Birch, Product Manager Google Earth Engine
USGSNASA—Copernicus

Brisbane, Australia, as viewed by Sentinel-2 (ESA)

Google Earth EngineGoogle Compute EngineGoogle Cloud Machine LearningDescartes Labs

Humboldt, Iowa (Landsat 8, USGS)

Agricultural field edge boundaries and field segmentation from July 2016 of Humboldt, Iowa, generated using machine learning and Landsat data on Google Cloud.

Spaceknow

Fuzhou, China 2000 (Landsat 7, USGS) Fuzhou, China 2016 (Landsat 8, USGS)

http://cloud.google.com/storage/docs/public-datasets/http://cloud.google.com/public-datasets/

Google Container Engine now on Kubernetes 1.4

Friday, September 30, 2016

Posted by David Aronchick, Senior Product Manager, Container EngineGoogle Container EngineKubernetes 1.4Google Cloud Platform

One-click alpha clusters can be spun up as easily as a regular cluster, so testing Kubernetes’ alpha features like persistent application support is a one-click operation.

Support for AppArmor in the base image gives applications deployed to Container Engine multiple layers of defense-in-depth.

Integration with Kubernetes Cluster Federation allows you to add a Container Engine cluster to your existing federation, greatly simplifying cloud bursting and multi-cloud deployments.

Rich support for Google Cloud Identity & Access Management allows you to manage GKE clusters with the same multi-faceted roles you use across your GCP projects.

A new Google Container-VM Image makes upgrading a breeze and allows new upgrades to be automatically installed with a simple reboot.

Monitoring of all cluster add-ons ensures that all key functions for your cluster are available and ready to use — one less thing to think about when running a large distributed application.

Niantic - creators of the global phenomenon Pokémon GO, relies on Container Engine to power their extraordinary growth.

Philips - smart connected lighting system Hue, receives 200 million transactions a day that are easily handled by Container Engine.

Google Cloud ML - the new Cloud Machine Learning service from GCP is also running fully on Container Engine.

And many more companies, from Box to Pearson, are choosing Kubernetes to manage their production workloads.

kubernetes-users-mailing listkubernetes-usersfree trial here

Introducing Google Container-VM Image

Friday, September 30, 2016

Posted by Aditya Kali and Amey Deshpande, Software EngineersContainer-VM ImageGoogle Cloud PlatformGoogle Compute Engine Container-VM Image design philosophy Build environment
Designed for containers More secure by design Software updates Getting started$ gcloud compute images list --project google-containers --no-standard-images$ gcloud compute instances create \ --zone us-central1-a \ --image-family gci-stable --image-project google-containers$ gcloud compute ssh --zone us-central1-a$ gcloud compute instances create \ --zone us-central1-a \ --image-family gci-stable --image-project google-containers \ --metadata-from-file user-data=<cloud-config-file> What’s nextContainer-VM Image homepage

Google Cloud Platform sets a course for new horizons

Thursday, September 29, 2016

Posted by Brian Stevens, Vice President, Google CloudGoogle Cloud Delivering Google Cloud Regions for allGoogle Cloud PlatformGoogle Cloud Regions——recent expansion in Oregon

Embracing the multi-cloud worldKubernetesreached version 1.4 earlier this weekGoogle Container-VM image (GCI)Philips Lighting Accelerating the move to cloud data warehousing and machine learning—

Google BigQuery, our fully managed data warehouse, has been significantly upgraded to enable widespread adoption of cloud data analytics. BigQuery support for Standard SQL is now generally available, and we’ve added new features that improve compatibility with more data tools than ever and foster deeper collaboration across your organization with simplified query sharing. We also integrated Identity and Access Management (IAM) that allows businesses to fine-tune their security policies. And to make it accessible for any business to use BigQuery, we now offer unlimited flat-rate pricing that pairs unlimited queries with predictable data storage costs.

Cloud Machine Learning is now available to all businesses. Integrated with our data analytics and storage cloud services such as Google BigQuery, Google Cloud Dataflow, and Google Cloud Storage, it helps enable businesses to easily train quality machine learning models on their own data at a faster rate. “Seeing is believing” with machine learning, so we're rolling out dedicated educational and certification programs to help more customers learn about the benefits of machine learning for their organization and give them the tools to put it into use.

Data Lifecycle on GCP paper Introducing a new engagement model for customer support Niantic as they launched Pokémon GO

The Google Cloud GKE/Kubernetes team that supports many of our customers like Niantic

Bringing Pokémon GO to life on Google Cloud

Thursday, September 29, 2016

Posted by Luke Stone, Director of Customer Reliability EngineeringGoogle CloudNiantic

A shared fateHorizon Creating the Pokémon game worldCloud Datastore Pokémon powered by containersGoogle Container Engine (GKE)Kubernetes projectNetwork Load BalancerHTTP/S Load Balancertripled

The Google Cloud GKE/Kubernetes team that supports many of our customers like Niantic

The Pokémon GO game world was brought to life using over a dozen services across Google Cloud.

Pokémon GO was the largest Kubernetes deployment on Google Container Engine ever. Due to the scale of the cluster and accompanying throughput, a multitude of bugs were identified, fixed and merged into the open source project.

To support Pokémon GO’s massive player base, Google provisioned many tens of thousands of cores for Niantic’s Container Engine cluster.

Google’s global network helped reduce the overall latency for Pokémon Trainers inhabiting the game’s shared world. Game traffic travels Google’s private fiber network through most of its transit, delivering reliable, low-latency experiences for players worldwide. Even under the sea!