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Yelp it! is the term people use to review a local business, restaurant or products across the main US states and cities. Yelp has grown from a simple reviews site to something much more. It is now a strong community of users who contribute reviews per their own volition. Now let us understand what does this mean in terms of data that is generated in Yelp. Since its inception in 2004, Yelp has collected a staggering 25 million reviews for its local businesses, restaurants, doctors, services, etc. They have an average of 66 million unique visitors to their site every month. Yelp App is used on 5.7 million mobile devices. They have an impressive Y-O-Y growth with reviews growing by 64%, visitors growing at 67%, local businesses at 97% and active local advertisers at 118%. That is a LOT of data! Phew!
It comes as no surprise when we say that Yelp has managed to crush all their competition mainly because they are so good at big data analysis. Data of this magnitude has a story to tell and businesses need to figure out what their data is telling them in order to make smarter business decisions than their competitors. In the following project, we have taken a Yelp data-set and we will be using Hive to analyze this data. Hive is the easiest of the Hadoop tools to learn. If you are from a data warehousing background and know SQL well - it will be a breeze to work on Hive. Hive is a data warehouse infrastructure built on top of Hadoop and is quite versatile in its usage, as it supports different storage types such as plain text, RCFile, Amazon S3, HBase, ORC, etc. Hive has its own SQL like language called HiveQL with schemas - which transparently converts queries to MapReduce or Apache Spark jobs.
You will be working on solving these business problems for the end-user:
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this Spark project, we are going to bring processing to the speed layer of the lambda architecture which opens up capabilities to monitor application real time performance, measure real time comfort with applications and real time alert in case of security
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.