pig vs hive comparison - Wikitechy

What is the difference between sqoop and pig ?

Editor — Thu, 22 Jul 2021 06:59:29 +0000

Pig:

Pig is a platform for managing large data.
It uses high level programming to analyse the data.
The programming is done using the language pig latin.
It is simple and highly specific.
The pig Metastore stores all info about the tables. And we can execute spark sql queries because spark can interact with pig Metastore

Sqoop:

Sqoop is a simple tool used for transferring large amount of data.
It is used for transferring data from hadoop to relational database.
sqoop can import data from Oracle,MYSQL,SQl.
Sqoop metastore is a tool for using hosts in a shared metadata repository. Multiple users and remote users execute saved jobs in metastore.

What is the main difference between pig vs hive vs sql ?

Editor — Mon, 12 Jul 2021 08:44:28 +0000

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.

What is the difference between Pig, Hive and HBase ?

Editor — Mon, 12 Jul 2021 08:40:35 +0000

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.

What is the difference between Pig and Sqoop in Hadoop ?

Editor — Mon, 12 Jul 2021 08:36:31 +0000

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.

What is the difference between Pig, Hive and MapReduce ?

Editor — Mon, 12 Jul 2021 08:26:57 +0000

Difference between Pig, Hive and MapReduce

Pig	Hive	Hadoop mapreduce
Pig is a scripting language	SQL like query language	It is a compiled language
Higher level of abstraction	Higher level of abstraction	Hadoop mapreduce used lower level of abstraction
Comparatively less line of codes than mapreduce	Comparatively less lines of code than mapreduce and apache pig	More lines of code
Development effort is less code efficiency is relatively less	Development effort is less code efficiency is relatively less	More development , effort is involved
Code efficiency is relatively less	Code efficiency is relatively less	Code efficiency is high when compared to pig and hive
Pig is open source	Hive open source	Hadoop MapReduce were built so that hadoop developers could do the same thing in Java in a less verbose way by writing only fewer lines of code that is easy to understand
Disadvantage of pig is that commands are not executed unless either you dump or store an intermediate or final result.This increases the iteration between debug and resolving the issue.	Disadvantages of hive is that No real time access to data, Updating data is complicated	The only drawback that developers need to write several lines of basic java code

What is the difference between Pig,Hive and Hadoop ?

Editor — Mon, 12 Jul 2021 08:18:28 +0000

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.

What is the best tool to process web streaming data in Hadoop,PIG or HIVE ?

Editor — Mon, 12 Jul 2021 08:07:43 +0000

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.

What is the history of Pig in Hadoop ?

Editor — Mon, 12 Jul 2021 05:12:12 +0000

History of Pig in Hadoop

In 2006 Pig was developed by Yahoo Research for particular way of creating and executing MapReduce jobs on very large data sets.
Apache Software Foundation was moved into 2007.
Pig started a project in Yahoo! Research, where Yahoo! scientists designed it and produced an implementation.
The MapReduce paradigm presented by Hadoop “is too low-level and rigid, and leads to a great deal of custom user code that is hard to maintain and reuse.”
Users were not comfortable with MapReduce languages such as SQL.
Sort the problem a new language called Pig Latin that we have designed to fit between the declarative style of SQL, and the low-level, procedural style of MapReduce.”
Hadoop users adopt Pig. So, a team of development engineers was assembled to take the research prototype and build into a production-quality product. About this same time, in fall 2007, Pig was open sourced via the Apache Incubator.
The first Pig release in September 2008, were Pig graduated from the Incubator and became a subproject of Apache Hadoop.