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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.
Explore hive usage efficiently in this hadoop hive project using various file formats such as JSON, CSV, ORC, AVRO and compare their relative performances
In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.
In this project, we will evaluate and demonstrate how to handle unstructured data using Spark.