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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 look at two such database platforms - MongoDB and Cassandra. These are two different databases or classes and have their use suited for different use cases. We will discuss these and install both platforms in our lab environment, look at the philosophical difference in how these databases work, create sample tables and finally integrate our spark application to load the UK MOT vehicle testing dataset into them. Once loaded, anyone can at any time, perform analytical queries on the tables.
In this project, we are going to talk about insurance forecast by using regression techniques.
In this project, we will show how to build an ETL pipeline on streaming datasets using Kafka.
In this project, we will take a look at three different SQL-on-Hadoop engines - Hive, Phoenix, Impala and Presto.