While making my first data stream through Kafka into Neo4j, I found it quite difficult to figure out exactly how to properly set everything up. I'd like to share what I've learned by showing and explaining parts of the docker-compose configuration of a proof of concept that I've made. The proof of concept showcases the power of Neo4j in a food traceability context.
Knowledge graphs have been taking the world of data science and engineering by storm, and for good reason. They make your data more transparent, connected and improve the accuracy of machine learning pipelines. This blog post will explain some example use cases of graphs in the data space.
We first discuss the future relevance of traceability as predicted by Gartner and McKinsey. Then, we showcase part of the power and flexibility of graph database technology Neo4j within the context of food traceability.
Right now, we are at the beginning of a new era in the industrial revolution, which is the most environmentally friendly so far. Now it is all about Smart Factory, autonomous systems, and networks. A large piece of all these innovations is a Digital Twin.
November 24, 2021
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