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.
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