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What is the difference between apache hive and apache spark?


Asked by Jesse Hammond on Nov 29, 2021 Spark Programming guide



The differences between Apache Hive and Apache Spark SQL is discussed in the points mentioned below: Hive is known to make use of HQL (Hive Query Language) whereas Spark SQL is known to make use of Structured Query language for processing and querying of data
Accordingly,
Apache Storm is the stream processing engine for processing real time streaming data while Apache Spark is general purpose computing engine which provides Spark streaming having capability to handle streaming data to process them in near real-time.
Subsequently, Spark can run on Apache Hadoop, Apache Mesos, Kubernetes, on its own, in the cloud—and against diverse data sources. One common question is when do you use Apache Spark vs. Apache Hadoop?
Likewise,
Beginning with Apache Spark version 2.3, Apache Arrow will be a supported dependency and begin to offer increased performance with columnar data transfer. If you are a Spark user that prefers to work in Python and Pandas, this is a cause to be excited over!
Next,
By adding support for arrow in sparklyr, it makes Spark perform the row-format to column-format conversion in parallel in Spark. Data is then transferred through the socket but no custom serialization takes place. All the R process needs to do is copy this data from the socket into its heap, transform it and copy it back to the socket connection.