Domino vs Google App Engine: What are the differences?
Domino: A PaaS for data science - easily run R, Python or Matlab code in the cloud with automatic version control for data, code, and results. Use our cloud-hosted infrastructure to securely run your code on powerful hardware with a single command — without any changes to your code. If you have your own infrastructure, our Enterprise offering provides powerful, easy-to-use cluster management functionality behind your firewall; Google App Engine: Build web applications on the same scalable systems that power Google applications. 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.
Domino and Google App Engine belong to "Platform as a Service" category of the tech stack.
Some of the features offered by Domino are:
- Domino Cloud supports the most powerful data analysis languages — Python, R, MATLAB, and Julia
- Modern and powerful cluster management
- Use a single-core machine during development
On the other hand, Google App Engine provides the following key features:
- Zero to sixty: Scale your app automatically without worrying about managing machines.
- Supercharged APIs: Supercharge your app with services such as Task Queue, XMPP, and Cloud SQL, all powered by the same infrastructure that powers the Google services you use every day.
- You're in control: Manage your application with a simple, web-based dashboard allowing you to customize your app's performance.
What is Domino?
What is Google App Engine?
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With Cloud Endpoints you can create and deploy mobile backend in one hour or less. And it is free (until you need extra scale). I would not recommend to use Java - python is faster and has all new appengine features.
Pros: everything is in one place: task queue, cron, backend instances for data processing, datastore, mapreduce. Cons: you cannot easily move your code from GAE. Even with special 3rd party services.
With Cloud Endpoints you can create and deploy mobile backend in one hour or less.
PaaS for back-end components, including external data ingestion APIs, front-end web service APIs, hosting of static front-end application assets, back-end data processing pipeline microservices, APIs to storage infrastructure (Cloud SQL and Memcached), and data processing pipeline task queues and cron jobs. Task queue fan-out and auto-scaling of back-end microservice instances provide parallelism for high velocity data processing.
checking a swap require a lot of cpu resource, roster normally come out same day of month, every month, at a particular time. Which make very high spike, our flag ship product, iSwap, with the capability looking swap possibility with 10000 other rosters base on user critieria, you need a cloud computing give you this magnitude of computing power. gae did it nicely, user friendly, easy to you, low cost.
App engine fills in the gaps in the increasingly smaller case where it's necessary for us to run our own APIs.
Very easy to make cloud computing of ML models , and use containers like Kubernetes.