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In the last hackerday, we looked at NoSQL databases and their roles in today's enterprise. We talked about design choices with respect to document-oriented and wide-columnar datbases, and conclude by doing hands-on exploration of MongoDB, its integration with spark and writing analytical queries using the MongDB query structures. Like we also noted, Spark has a benefit of being very extensible to quite a number of storage platforms beyond hadoop. This means that as spark developers, we can write and read from virtually any popular storage platform while building our data pipeline. In this hackerday, we will conclude that session by take a look at Cassandra. We will look at what it is suited for especially in a hadoop environment, how to integrate it with spark, installation in our lab environment, modelling the UK MOT vehicle testing dataset that we used on MongoDB in the first part. Once loaded, anyone can at anytime, perform analytical queries on the tables.
In this NoSQL project, we will use two NoSQL databases(HBase and MongoDB) to store Yelp business attributes and learn how to retrieve this data for processing or query.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
In this project, we will evaluate and demonstrate how to handle unstructured data using Spark.