Graph Databases, a little connected tour
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| Title | Graph Databases, a little connected tour |
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| Part Number | 59 |
| Number of Parts | 119 |
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| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. |
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| Production Place | Berlin |
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| Abstract | Francisco Fernández Castaño - Graph Databases, a little connected tour
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.
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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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