1. Apache Hive :
Apache Hive is a data warehouse device constructed on the pinnacle of Apache Hadoop that enables convenient records summarization, ad-hoc queries, and the evaluation of massive datasets saved in a number of databases and file structures that combine with Hadoop, together with the MapR Data Platform with MapR XD and MapR Database. Hive gives an easy way to practice structure to massive quantities of unstructured facts and then operate batch SQL-like queries on that data.
2. Apache Spark SQL :
Spark SQL brings native assist for SQL to Spark and streamlines the method of querying records saved each in RDDs (Spark’s allotted datasets) and in exterior sources. Spark SQL effortlessly blurs the traces between RDDs and relational tables. Unifying these effective abstractions makes it convenient for developers to intermix SQL instructions querying exterior information with complicated analytics, all inside a single application.
Difference Between Apache Hive and Apache Spark SQL :
|S.No.||Apache Hive||Apache Spark SQL|
|1.||It is an Open Source Data warehouse system,|
constructed on top of Apache Hadoop.
|It is used in structured data Processing system where|
it processes information using SQL.
|2.||It contains large data sets and stored in Hadoop files for|
analyzing and querying purposes.
|It computes heavy functions followed by correct|
optimization techniques for processing a task.
|3.||It was released in the year 2012.||It first came into the picture in 2014.|
|4.||For its implementation, it mainly uses JAVA.||It can be implemented in various languages such as R, Python and Scala.|
|5.||Its latest version (2.3.2) is released in 2017.||Its latest version (2.3.0) is released in 2018.|
|6.||Mainly RDMS is used as its Database Model.||It can be integrated with any No-SQL database.|
|7.||It can support all OS provided, JVM environment will be there.||It supports various OS such as Linux, Windows, etc.|
|8.||Access methods for its processing include JDBC, ODBC and Thrift.||It can be accessed only by ODBC and JDBC.|