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.
Windows Azure Service Bus is a brokered, scalable, multi-featured messaging queuing system. It's a reliable message queuing and durable publish/subscribe system.
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!
Apache Hive is a powerful tool for analyzing data. It's very important that you know how to improve the performance of query when you are processing petabytes of data.
In-memory databases are a common occurrence with unit tests, so let's take a look at how to remotely connect to an instance if you need to, say, debug some data or tests.
Need to query within embedded documents? This example takes us through using Couchbase's N1QL query for objects in a nested array in a single document.
When it comes to the Enterprise, two cloud databases are going after it. Amazon's Aurora is MySQL compatible, whereas Google's CloudSpanner scales out.