GRAKN.AI is a deductive database in the form of a knowledge graph that uses machine reasoning to simplify data processing challenges for AI applications.
Although ''microservices'' might seem like a buzzword, I suggest taking advantage of the modernized techniques that the microservices movement is generating.
Securing cloud-based IoT is hard; there is a combination of local software, cloud, and hardware solutions to deal with. Let's take a look at a possible solution.
There are only 11 steps to POSTing JSON data to a Mule flow. JSON data can be sent directly to a Mule flow. The reason for doing this is to access data from a payload.
Windows Azure Service Bus is a brokered, scalable, multi-featured messaging queuing system. It's a reliable message queuing and durable publish/subscribe system.
LinkedList is slightly slower than ArrayList with adding items to the end of the list. It is also slower when retrieving items with an index (random access).
Uncle Bob's Clean Architecture keeps your application flexible, testable, and highlights its use cases. But there is a cost: No idiomatic framework usage!
Spring Boot and Swagger 2 play together very well. Just add the dependencies, one configuration file, and a bunch of annotations, and you're ready to go!
When you are going to add DataSense to a custom connector, having configuration declaration is mandatory irrespective of whether the configuration is mandatory.
With cloud-native microservices, you can develop, test, deploy, and maintain independent lightweight services while combining various other technologies.
Test your backup and restore procedures right after you install your cluster. Backups are a waste of time and space if they don't work and you can't get your data back!