Modeling & Thinking in Graphs(Neo4J) using Movielens Dataset

In this big data project using Neo4j, we will be remodelling the movielens dataset in a graph structure and using that structures to answer questions in different ways.
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What will you learn

  • Thinking in terms of Graphs
  • Data modeling in a Graph database
  • Neo4J and the CAP theorem
  • Discuss possible scenarios of integrating Neo4J with a big data pipeline.
  • Migrating data from CSV to Neo4J database
  • Writing queries in Cypher in Neo4J database
  • Using Spring Data Neo4J to interact with a Neo4J database
  • Implement a simple recommendation engine

Project Description

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.

Curriculum For This Mini Project

 
  30-Oct-2016
02h 45m