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With Sigma, serverless development is as simple as drag-and-drop, filling in the parameters, writing your code, and clicking Deploy! It takes care of everything else; I never have to go into all sorts of management consoles to define my entities, create/edit lengthy configuration files, or even go through a load of docs and tutorials. It's a great place to get started on your serverless ride, especially for a beginner. See More
Janaka Bandara's Experience
Sigma needs just an AWS account (for deploying your serverless app) and GitHub account (for saving your code and generated configurations). But even if you don't have either or both of them, you can still get started with a free demo account or one of the ready-made samples. See More
With Sigma, you can define your entities - buckets, databases, queues, APIs, etc. - as and when you encounter them in your code (reusing the previously defined ones when necessary). This ensures a seamless development experience, without any jump-arounds among multiple dashboards, documentation pages and configuration files. See More
Sigma seamlessly integrates with your GitHub account, so you can save and reload your projects from GitHub at any time. Sigma can also load public projects from any GitHub URL or profile, so sharing your latest adventure with your colleagues is just a matter of sharing your GitHub URL. See More
App Engine brings with it a number of APIs that can lead to confusing choices; for example, outside of App Engine, there is pretty much one clear choice in terms of how to use the Cloud Datastore API: one uses the proper client library (e.g. in Python, this would be the google-cloud-datastore library in PyPi). On App Engine, however, there are often multiple APIs; for example, in Python, there is both the "db" and "ndb" libraries provided by App Engine (easier to use), as well as the "google-cloud-datastore" library (harder to get working in App Engine). See More
Some of the App Engine environments have compatibility issues using certain APIs. For example, in App Engine Python Standard, it can be challenging to use many of the Google Cloud Python client libraries due to the 'google.cloud' namespace being used incompatibly between the Google Cloud Python client library code and between the APIs that are provided by the App Engine environment. Likewise, code making socket connections or other HTTP requests without going through the proper App Engine APIs can run into issues. See More
App Engine is vendor-specific to Google. Some code that works with App Engine may need to be modified or re-written to bring to other providers. Not only that, but even among Google's various App Engine environments, some modification may be necessary to port code between "standard" and "flexible" environments. For maximum portability, you may want to consider using "Cloud Run", instead, which brings many of the benefits of App Engine while, at the same time, providing a much more portable approach to running code. See More
Google App Engine is very easy to use. All you need to do is install the SDK (which in itself is easy as well, and the documentation is very heplful) and run the command needed depending on the type of project to deploy it. For example, to deploy a golang application, you run golang deploy inside the project folder and it will be automatically deployed. See More
Open source serverless platform that looks very promising. See More
Cloud Run provides the best of App Engine standard (scale to zero), App Engine flexible (use whatever dependencies you need), and Kubernetes (make things standard and portable) while being simple and easy to use. It provides greater security than Google Cloud Functions (which, by default, have an endpoint that is externally exposed). I would personally recommend targeting Cloud Run for all future workloads (over other compute environments). See More
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