- The premise here is: You have some data, you’d like to make it available over the Internet, how hard is this?
- The good news is: You can be up and running with a toy example in a matter of a day
- The reality is: Building a full-up data service you are happy with is likely to be something of a project
- Therefore: Be sure to have a demand that justifies the time investment to build your supply
- Let’s quickly outline three approaches to consider. We elaborate on the second and third approaches below.
- First approach: Place the data in a logical structure on the cloud and enable your “customers” to read it
- Simplest; but your data users need to build compute environments on the cloud
- Second approach: Place the data on the cloud and build a data service using a Web Framework
- Third approach: Place the data on the cloud and build a data service using Serverless functions
- First approach: Place the data in a logical structure on the cloud and enable your “customers” to read it
- Web Framework approach to data as a service
- A Web Framework is an assembly of code and libraries. We install it to publish a generic interface; and then we customize
- A good Web Framework to read about is called Flask: It is not too complicated, nor is it too super-simple
- A really powerful Web Framework, in contrast is Django; much bigger time investment needed to climb the learning curve
- Serverless approach to data as a service
- Serverless compute is a simple way to build some intelligence into a data service
- Think of serverless as “code that magically runs and I don’t have to worry about the computer or the operating system”
- To do: Provide the link to the re-tested serverless tutorial “Zero2API”
- A blog on data science practice containing some useful links…
