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Graph Databases, a little connected tour

Summary

There are many kinds of NoSQL databases like, document databases, key-value, column databases and graph databases. In some scenarios is more convenient to store our data as a graph, because we want to extract and study information relative to these connections. In this scenario, graph databases are the ideal, they are designed and implemented to deal with connected information in a efficient way.

Description

There are many kinds of NoSQL databases like, document databases, key-value, column databases and graph databases. In some scenarios is more convenient to store our data as a graph, because we want to extract and study information relative to these connections. In this scenario, graph databases are the ideal, they are designed and implemented to deal with connected information in a efficient way. In this talk I'll explain why NoSQL is necessary in some contexts as an alternative to traditional relational databases. How graph databases allow developers model their domains in a natural way without translating these domain models to an relational model with some artificial data like foreign keys and why is more efficient a graph database than a relational one or even a document database in a high connected environment. Then I'll explain specific characteristics of Neo4J as well as how to use Cypher the neo4j query language through python.

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