Apache Hive provides a SQL-like interface to query and manipulate data stored in Hadoop, while Apache Pig provides a scripting language to define data flows and transformations. Hive is better suited for business intelligence analysis and ad-hoc queries through its familiar SQL interface, while Pig is more appropriate for data pipelines, iterative data processing, and research through its scripting capabilities. Both can perform similar functions but may differ in performance depending on the use case.

1. By Thanuja Seneviratne

2.  Terminology
› Apache Hive - provides a SQL-like interface (supported with a
language) to data stored in Hadoop
› Apache Pig – provides a Scripting language for data flows in the
data stored in Hadoop
 What is Hive?
 What is Pig?
 Demo

3.  Provides a SQL-like interface to data stored in Hadoop
 Provides a data workbench where you can examine,
modify and manipulate the data
 Hive is considered friendlier and more familiar to users
who are working on SQL for querying data.
 In general, any task can be done in Pig can be achieved
from Hive as well and vice versa
 But depend on the use case Hive or Pig can result
better in performance than the other
 Hive friendly use-cases (data warehouse type cases):
› business-intelligence analysis
› ad-hoc queries

4.  Provides a Scripting language for data flows in the data
stored in Hadoop
 Data objects exist and are operated on in the script.
Once the script is complete all data objects are deleted
unless you stored them
 Pig friendly use-cases (data factory type cases):
› data pipelines - bring in a data feed, and clean and transform
› iterative processing – bring in small dataset changes so that the state
of a large dataset changes iteratively
› research – test new theories and hypotheses using script quickly.

5.  Hive Demo
› Using CDH
› Using HDP
 Pig Demo
› Using CDH
› Using HDP