pig vs sql - Wikitechy https://www.wikitechy.com/interview-questions/tag/pig-vs-sql/ Interview Questions Tue, 14 Sep 2021 11:28:14 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png pig vs sql - Wikitechy https://www.wikitechy.com/interview-questions/tag/pig-vs-sql/ 32 32 What is the difference between Pig and Sqoop in Hadoop ? https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-and-sqoop-in-hadoop/ https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-and-sqoop-in-hadoop/#respond Mon, 12 Jul 2021 08:36:31 +0000 https://www.wikitechy.com/interview-questions/?p=209

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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Best tool to process web streaming data in Hadoop or PIG or HIVE :

  • It contains easy programming.
  • It is little to achieve parallel execution of simple, “embarrassingly parallel” data analysis tasks.
  • For Complex tasks it comprised the multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.
  • To upgrade their execution automatically, allowing the user to focus on semantics rather than efficiency and Extensibility.
  • A special-purpose processing to create own functions.
  • For efficient working pig and hive can be used together.
  • Pig is best tool for parsing (ETL) kind of job, and even pig supports UDF better than hive.
  • It can help you to develop you own framework using pig where you can call Hive ql and map-reduce job for better functionality.
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