Difference between Apache Hive and Apache Spark
| Apache-Hive | Apache-Spark |
|---|---|
| Apache hive is a distributed query engine built on top of the hadoop eco-system. Hive is introduced by facebook and later on they open sourced it. |
Apache spark may be a cluster computing framework that runs on Hadoop and handles differing kinds of data. |
| The language used by hive to write queries is HQL i.e., Hive+SQL. |
Spark has rich resources for handling the data and most importantly, it is 10-20x faster than Hadoop’s MapReduce. |
| These hive queries can internally get converted into MapReduce job and this is fully handled by the Hadoop framework reducing the work load on the developers. |
It attains this speed of computation by its in-memory primitives. The data is cached and is present in the memory (RAM) and performs all the computations in-memory. |
| Tools to alter easy access to data via SQL, so enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis. |
It is fast and grouping the computing system. It provides high-level in Java, Scala, Python,R, and an optimized engine that supports execution graphs. |
| Access files to stored either directly in Apache HDFS or such as Apache HBase |
Spark structured query language is a Spark module for structured data processing, in which in-memory processing is its core. |