The use of a graph database allows for ad hoc querying and visualization, which has proven very valuable when working with domain experts to identify interesting patterns and paths. Using Hadoop again for the heavy lifting, we can do traversals against the graph without having to limit the number of features (attributes) of each node or edge used for traversal. The combination of both can be a very productive workflow for network analysis.In this video, he demonstrates the workflow of using Hadoop to create a graph out of data and bulk load the result into Neo4j for efficient ad hoc querying and visualization, potentially partitioning the graph in Hadoop, to create partitions of manageable volume for the database.
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