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Graph databases provide us with a new paradigm and thinking process in storage and analyzing data. Furthermore, we are now exposed to more powerful intuitions in querying data that would have required a few more processing steps.
In the arena of big data processing with graph frameworks like Apache Giraph or Apache Spark GraphX library, it is possible that intermediate and/or reusable results could be stored in a structure that is usable either in a downstream data processing pipeline or in the serving layer of a lambda architecture implementation.
In this Neo4j project, we will be remodeling the movielens dataset in a graph structure and using that structures to answer questions in different ways. We will explore graph databases, designing a graph database and reasons why it would be preferred to other traditional forms of databases, explore Neo4J as an open source leader in graph database structure as well as learning the language to interact with neo4j (cypher) and will attempt to build a simple (not-sophisticated) recommendation engine based on the data.
Finally, using the spring data neo4j framework, we will build a simple backend Java Restful Web Service to drive home the point that Neo4J could really play in the lambda architecture.
In this Neo4j project, you will do network analysis using a graph database to find patterns on how a social network affects business reviews and ratings.