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A movie recommendation system is used by top streaming services like Netflix, Amazon Prime, Hulu, Hotstar etc to recommend movies to their users based on historical viewing patterns.
Before the final recommendation is made, there is a complex data pipeline that brings data from many sources to the recommendation engine. In this project, we use Databricks Spark on Azure with Spark Sql to build this data pipeline.
Our dataset is from GroupLens Research, which is a research group in the Department of Computer Science and Engineering at the University of Minnesota. They operate a movie recommender based on collaborative filtering called MovieLens. This dataset (ml-latest) describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. It contains 22884377 ratings and 586994 tag applications across 34208 movies. These data were created by 247753 users between January 09, 1995 and January 29, 2016. This dataset was generated on January 29, 2016.
In this big data project, we will look at how to mine and make sense of connections in a simple way by building a Spark GraphX Algorithm and a Network Crawler.
This is in continuation of the previous Hive project "Tough engineering choices with large datasets in Hive Part - 1", where we will work on processing big data sets using Hive.
In this big data project, we will be performing an OLAP cube design using AdventureWorks database. The deliverable for this session will be to design a cube, build and implement it using Kylin, query the cube and even connect familiar tools (like Excel) with our new cube.