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Spark SQL offers the platform to provide a structured data to any dataset regardless its source or form. And once that structured data is formed, it can be queried using tools like Hive, Impala, and other Hadoop data warehouse tools.
In this spark project, we will go through Spark SQL syntax to process the dataset, perform some joins with other supplementary data as well as make the data available for the query using the Spark SQL thrift server. On provision of the data, we will perform some interesting query and other go through some performance tuning technique for Spark SQL.
The goal of this spark project for students is to explore the features of Spark SQL in practice on the latest version of Spark i.e. Spark 2.0.
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.
This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.