Comparison between Hive, Impala, Drill and SparkSQL

	Hive	Impala	Drill	SparkSQL
Project Goal	Offline batch processing stuff; Long running job performing data heavy operation, such as joins on huge data sets	Run real-time queries on top of existing Hadoop warehouse	Provides distributed query capability across multiple big data platform. Query data from any or all of those data sources at the same time and can push down into the underlying storage system.	Execute SQL query, then deal with the result sets.
Similarity	Impala is designed based on Hive. Using the same metadata. All designed for Hadoop env.		Support query data from a variety of different datasources. (RDBMS, NoSQL, File, JSON...) All support JDBC/ODBC drivers.
Difference	Suitable for Offline data processing	Focus on online real-time data processing	Not only hadoop project
			Schema Free: all data is internally represented as either a simple or complex JSON data structure
			Fully support SQL Query (ANSI SQL:2003)	Just have SQL query capabilities Subset of SQL (SQL-Like)
			Supported by many BI tools
				Better security support for data accessing

References:

https://www.javacodegeeks.com/2015/12/apache-spark-vs-apache-drill.html