Maybe you’ve heard of them before, knowledge graphs. But if you haven’t, no worries, you are not alone. Knowledge graphs take an entirely new approach to data management. What makes them stand out from other data solutions is that they focus on the meaning and context of data by extracting the purpose of the language.

The use cases are almost endless. It allows scientists to automate drug discovery, doctors to search for diseases based on patients’ symptoms, to map our complex Internet of Things landscapes, gather insights from billions of financial transactions, and many more things.

During this talk, we will go over the Weaviate software, the cloud deployment on Google Cloud Platform, and the use cases. Needless to say, they are all supported by on-stage demos. When the talk is over, you will be ready to start building your own Weaviate knowledge graph on Google Cloud Platform the very next day.

Weaviate

Weavite is an open source knowledge graph that stores data in a vector space rather than in a traditional DB or graph-DB. Every time you add a data object, Weaviate interprets the semantic meaning and assigns it the right vector space. Thanks to the handy GraphQL interface, you can easily query the knowledge graph for its insights and integrate it into your applications. Weaviate is fast, easy to use, and entirely API-based.

Google Cloud Platform

At the lowest level, Weaviate runs on Kubernetes, which makes it ideal for running on the Google Cloud Platform. From a simple development setup to a full-blown enterprise stack, it all runs out of the box.