Discover how Neo4j graph database company can compete with machine learning and AI.
When it comes to machine learning and the GraphDB database system, " We’re in the first inning of a nine-inning game," said Emil Eifrem, CEO and founder of Neo4j. "If you look at machine learning algorithms, they are written in graph language, or can be expressed in graph language."
Speaking from Fort Mason in San Francisco at the GraphTour event earlier this year, Eifrem talks about graph databases, how they are different from relational databases, and how this decade-old technology is keeping up with the new kids on the block.
Graph databases are about connecting data so that it can be accessed by its relationship to other data. The Neo4j graph database began as an idea from seeing a specific pain point at a small startup in Sweden. "Half of our engineers spent their time fighting against the relational database," he explained. All of sudden, the database had gone from an accelerator to a stumbling block. The solution was getting away from the relational database model.
Eifrem talks in the podcast about graph databases
"We realized that if we had just the very simple building blocks of nodes, relationships between those nodes then key-value pairs that can attach to both the nodes and the relationships, then you can model everything."
Fifteen years later, it turns out it is hard to build a database from scratch, which is why they are the leader in the field by a large margin.
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