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In this spark project, we will continue building the data warehouse from the previous project Yelp Data Processing Using Spark And Hive Part 1 and will do further data processing to develop diverse data products.
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Business Overview:
This is Part 2 of Yelp Data Processing using Spark and Hive project. In Part 1, we started with the development of Yelp dataset into domains that can easily be understood and consumed. In this project, we will continue building the data warehouse from the previous project and will do further data processing to develop diverse data products. For this project, we will use Amazon EMR which is an alternative to the Hadoop cluster in AWS and S3 where our data is stored.
Yelp is a community review site and an American multinational firm based in San Francisco, California. It publishes crowd-sourced reviews of local businesses as well as the online reservation service Yelp Reservations. Yelp has made a portion of their data available in order to launch a new activity called the Yelp Dataset Challenge, which allows anyone to do research or analysis to find what insights are buried in their data. Due to the bulk of the data, this project only selects a subset of Yelp data. User and Review dataset is considered for this session.
Tech Stack:
Language: Spark, Scala.
Services: Amazon EMR, Hive, HDFS, AWS S3
Approach:
Create a S3 bucket and upload files
Create a keypair in EC2
Create an EMR cluster with master and slave nodes along with Spark, Hive components
Basic Dataframe operations like Read and write to tables and hdfs locations
Hive Integration from spark
Normalizing data using RDD operations
Normalizing data using Dataframe operation
Architecture diagram:
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