Modeling & Thinking in Graphs(Neo4J) using Movielens Dataset

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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Mohamed Yusef Ahmed

Software Developer at Taske

Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More

James Peebles

Data Analytics Leader, IQVIA

This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More

What will you learn

Understanding the basics of Graphs and terminologies related to it
Thinking in terms of Graphs
Using Cypher as Neo4j query language
Data modeling in a Graph database
How to install Neo4J in your system
Neo4J and the CAP theorem
Discuss possible scenarios of integrating Neo4J with a big data pipeline.
Understanding relationships between different elements in terms of Graphs
Visualizing Nodes and Branches in the Movielens Dataset
Migrating data from CSV to Neo4J database
Extracting desired data for analysis by writing queries in Cypher
Using Spring Data Neo4J to interact with a Neo4J database
Concatenating Different queries for obtaining the final result
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.

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Curriculum For This Mini Project

02h 45m