pig vs mapreduce - Wikitechy https://www.wikitechy.com/interview-questions/tag/pig-vs-mapreduce/ Interview Questions Tue, 14 Sep 2021 11:25:08 +0000 en-US hourly 1 https://wordpress.org/?v=6.9 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png pig vs mapreduce - Wikitechy https://www.wikitechy.com/interview-questions/tag/pig-vs-mapreduce/ 32 32 What is the difference between hive and pig ? https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-hive-and-pig/ https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-hive-and-pig/#respond Tue, 13 Jul 2021 22:18:37 +0000 https://www.wikitechy.com/interview-questions/?p=600

Difference between hive and pig

Hive pig
Hive is used by data analysts. Pig Hadoop Component is generally used by Researchers and Programmers.
Hive is used in structured Data Pig Hadoop Component is used for semi structured data.
Hive Hadoop Component has a
declarative SQL language (HiveQL).		 Pig has a procedural data flow language (Pig Latin).
Hive uses thrift based server that send queries
and corner directly to the Hive
server which execute them.		 This feature is not available with Pig.
Hive directly leverages SQL expertise
and thus can be learnt easily.		 Pig is also SQL but varies to a great extent and it will take some time efforts to master Pig.
Hive not support in Avro. Pig supports in Avro.
Hive Hadoop Component operates
on the server side of any cluster.		 Pig Hadoop Component operates on the client side of any cluster.
Hive Hadoop Component is mainly
used for creating reports.		 Pig Hadoop Component is mainly used for programming.
Hive helpful for ETL. Pig is a great (Extract, Transform and Load) tool for
big data its powerful transformation and processing capabilities.
Hive makes use of exact variation of the
SQL DLL language by defining the tables beforehand
and storing the schema details in any local database.		 In Pig there is no dedicated metadata database and the schemas or data types will be defined in script itself.
The Hive has a provision for partitions so that can
process the subset of data by date
or in an alphabetical order.		 Pig Hadoop component does not have any notion for partitions though might be one can achieve this through filters.
It renders users with sample data for each
scenario and each step through
its “Illustrate” function.		 This feature is not incorporated with the Hive Hadoop Component.
]]> https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-hive-and-pig/feed/ 0 What is the main difference between pig vs hive vs sql ? https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-main-difference-between-pig-vs-hive-vs-sql/ https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-main-difference-between-pig-vs-hive-vs-sql/#respond Mon, 12 Jul 2021 08:44:28 +0000 https://www.wikitechy.com/interview-questions/?p=213

Difference between pig vs hive vs sql

Pig Hive Sql
Apache Pig also allows developers
to follow multiple query approach,
which reduces the data scan
iterations.		 Hive statements are similar to
Structured Query Language (SQL)
and limitations of Hive Query
Language(HQL) in terms of
the commands that it
understands, it is still very useful.		 Structured Query Language has been a programmer’s companion for decades. It was the real solution for extractingdata for further processing.
PigLatin is procedural to a
large extent.		 Hive is built with an analytical focus. Structured Query Language is Informative.
Pig requires that programmers
to learn something on top of
structured query language(SQL).		 Hive provides an open source
implementation of MapReduce.		 Structured Query Language demand of storing data
in a strict relational database
schemas and its declarative nature often deflects
focus from the ultimate purpose to extract data for analysis.
It offers a number of advantages
in terms of declaring execution plans
Extract, transform, load(ETL)
routines and pipeline modification.		 Hive lacks update and delete
functions but is superfast in reading
and processing huge volumes of data
faster than SQL.		 The DBMS systems that SQL operates on,
are considered to be faster than MapReduce.
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Difference between Pig, Hive and HBase

Pig Hive Hbase
It is used for semi structured data. Hive is query engine HBase is a data storage particularly for unstructured data.
Pig Hadoop Component is generally
used by Researchers and Programmers.		 Apache Hive is mainly used for
batch processing i.e. OLAP
and creating reports.		 HBase is extensively used for transactional processing where in the response time of the query is not highly interactive i.e. OLTP
Pig Hadoop Component operates on
the client side of any cluster.		 Hive Hadoop Component operates on
the server side of any cluster.		 Operations in HBase are run in real-time on the database
Avro supported for Pig. Hive does not support Avro. The client which is reading/writing the data has to deal with the avro schemas, after HBase delivered the raw data to it.
Pig Hadoop is a great ETL tool
for big data because of its powerful
transformation and processing
capabilities.		 Hive Hadoop Component is helpful
for ETL.		 Hbase Component is helpful for ETL.
Pig are high-level languages that
compile to MapReduce.		 Hive is also a high-level languages
that compile to MapReduce.		 HBase allows Hadoop to support the transactions on key value pairs.
Pig is also SQL-like but varies to
a great extent and thus it will
take some time efforts to master Pig.		 Hive directly leverages SQL expertise
and thus can be learnt easily.		 HBase allows you to do quick random versus scan all of data sequentially, do insert/update/delete from middle, and not just add/append.
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Difference between Pig and Sqoop in Hadoop

Pig Sqoop
Apache Pig is a tool for analytics which is used to
analyze data stored in HDFS.		 Apache Sqoop is a tool to importing structured data from RDBMS to HDFS or exporting data from HDFS to RDBMS.
We can import the data from Sql databases into
hive rather than NoSql Databases.		 It can integrate with any external data sources with HDFS i.e Sql , NoSql and Data warehouses as well using this tool at the same time we export it as well since this can be used as bi-directional ways
Pig can be used for following purposes ETL
data pipeline, Research on raw data.		 Important Sqoop control commands to import RDBMS data are Append, Columns and Where
The pig Metastore stores all info about the tables.
And we can execute spark sql queries because spark
can interact with pig Metastore.		 Sqoop metastore is a tool for using hosts in a shared metadata repository. Multiple users and remote users can define and execute saved jobs defined in metastore.
The scalar data types in pig are int, float,
double, long, chararray, and bytearray. The complex data
types in Pig are map, tuple, and bag.		 It basically converts CHAR(x), VARCHAR(x), NUMERIC(x,y) to string (with lengh 32767), and it converts DATETIME to BIGINT.
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Difference between Pig,Hive and Hadoop

Pig Hive Hadoop
Apache Pig is a high-level
platform for creating programs
it runs on Apache Hadoop.		 Apache Hive is a data warehouse
software project built on top of
Apache Hadoop for providing
data query, and
analysis.		 Open-source software framework for distributed storage and processing dataset of big data using the MapReduce programming model.
Pig Latin extracts the programming
from the Java MapReduce phrase
into a code which creates
MapReduce programming high level.		 Hive provides the SQL
abstraction to integrate
SQL-like queries (HiveQL).
The underlying Java without
we need to implement queries in the
low-level Java API.		 The Hadoop Common package for Java ARchive (JAR) files and scripts needed to start Hadoop.
Pig basically has 2 parts:
the Pig Interpreter and the
language, PigLatin		 It provides us data warehousing
facilities on top of an
existing Hadoop cluster.		 Hadoop is basically two things:Hadoop Distributed File System (HDFS) +a Computation or Processing framework (MapReduce).
Pig makes our life a lot easier,
otherwise writing MapReduce is
always not easy.		 User can create tables in Hive and
store data there. Along with
that user can even map their
existing HBase tables to Hive
and operate on them.		 Hadoop is a distributed, scalable,big data store, modelled after Google’s BigTable.It stores data as key/value pairs.
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