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		<title>What is the advantages of VB.NET ?</title>
		<link>https://www.wikitechy.com/interview-questions/vb-net/what-is-the-advantages-of-vb-net/</link>
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		<pubDate>Thu, 22 Jul 2021 07:43:21 +0000</pubDate>
				<category><![CDATA[VB.NET]]></category>
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					<description><![CDATA[Answer : VB.NET offers code execution that runs under the Common Language Runtime (CLR)...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading" style="text-align: justify;"></div>
<div class="Content">
<div class="hddn">
<ul>
<li style="text-align: justify;">VB.NET offers code execution that runs under the Common Language Runtime (CLR), resulting in robust, stable and secure applications. Both features of the .NET framework are eagerly obtainable in VB.NET.</li>
<li style="text-align: justify;">VB.NET is object oriented. This is a major addition that VB6 and other earlier releases didn&#8217;t have.</li>
<li style="text-align: justify;">The .NET framework comes with ADO.NET, which follows the disconnected paradigm, i.e. once the required records are fetched the connection no longer exists.</li>
<li style="text-align: justify;">It also retrieves the records that are expected to be accessed in the immediate future.</li>
<li style="text-align: justify;">This enhances Scalability of the application to a great extent.</li>
<li style="text-align: justify;">VB.NET uses XML to transfer data between the various layers in the DNA Architecture i.e. data are passed as simple text strings.</li>
<li style="text-align: justify;">Error handling has changed in VB.NET. A new Try-Catch-Finally block has been introduced to handle errors and exceptions as a unit, allowing appropriate action to be taken at the place the error occurred thus discouraging the use of ON ERROR GOTO statement. This again credits to the maintainability of the code.</li>
</ul>
</div>
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		<title>What is the difference between vb and vb.net ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Thu, 22 Jul 2021 07:41:06 +0000</pubDate>
				<category><![CDATA[VB.NET]]></category>
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					<description><![CDATA[Answer : VB stands for Visual Basic. It is a high level programming...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="" class="color-pink"></h2>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>VB</th>
<th>VB.NET</th>
</tr>
<tr>
<td class="text-leftalign">VB stands for Visual Basic.<br />
It is a high level programming language<br />
that is used for the fast development<br />
of Windows based programs.</td>
<td class="text-leftalign">VB.NET stands for Visual Basic Network Enabled Technology. It is one of the languages for the .NET framework by Microsoft. This language has been created specifically for VB developers. It belongs to the category of object oriented computer programming languages that is implemented on the .NET framework.</td>
</tr>
<tr>
<td class="text-leftalign">Interpreter based Language</td>
<td class="text-leftalign">Compiled language</td>
</tr>
<tr>
<td class="text-leftalign">VB Runtime</td>
<td class="text-leftalign">Common Language Specification</td>
</tr>
<tr>
<td class="text-leftalign">This type of language is not safe.</td>
<td class="text-leftalign">This language is safe compared to visual basic.</td>
</tr>
<tr>
<td class="text-leftalign">Multithreaded applications Cannot be developed.</td>
<td class="text-leftalign">Multithreaded applications Can be developed.</td>
</tr>
<tr>
<td class="text-leftalign">Data is handled using DAO protocol, then RDO,<br />
followed by ADO (ActiveX Data Objects)</td>
<td class="text-leftalign">Data is handled using ADO.net</td>
</tr>
<tr>
<td class="text-leftalign">An object based language.</td>
<td class="text-leftalign">A true object oriented language.</td>
</tr>
<tr>
<td class="text-leftalign">Most of the parameters passed by reference.</td>
<td class="text-leftalign">Parameters passing by Default value.</td>
</tr>
<tr>
<td class="text-leftalign">Objects support default property</td>
<td class="text-leftalign">Objects does not support default property</td>
</tr>
<tr>
<td class="text-leftalign">Replaced by elements in Collection QBColor,<br />
RGB DateAdd, DateDiff, DatePart, DateSerial,<br />
DateValue, TimeSerial, TimeValue</td>
<td class="text-leftalign">System.Collection System.Drawing.Color Replaced by elements in System.DateTime</td>
</tr>
<tr>
<td class="text-leftalign">End Type structure can be used in declare function.</td>
<td class="text-leftalign">Declared using the Structure&#8230;End Structure construct.</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="text-center row"></div>
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		<title>What is the difference between sqoop and pig ?</title>
		<link>https://www.wikitechy.com/interview-questions/sqoop/what-is-the-difference-between-sqoop-and-pig/</link>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Thu, 22 Jul 2021 06:59:29 +0000</pubDate>
				<category><![CDATA[Sqoop]]></category>
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					<description><![CDATA[Answer : Sqoop is a simple tool used for transferring large amount of data...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="pig" class="color-purple" style="text-align: justify;"><span style="font-family: inherit; font-size: 2rem;">Pig:</span></h2>
</div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>Pig is a platform for managing large data.</li>
<li>It uses high level programming to analyse the data.</li>
<li>The programming is done using the language pig latin.</li>
<li>It is simple and highly specific.</li>
<li>The pig Metastore stores all info about the tables. And we can execute spark sql queries because spark can interact with pig Metastore</li>
</ul>
</div>
</div>
<div class="subheading" style="text-align: justify;">
<h2 id="sqoop">Sqoop:</h2>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>Sqoop is a simple tool used for transferring large amount of data.</li>
<li>It is used for transferring data from hadoop to relational database.</li>
<li>sqoop can import data from Oracle,MYSQL,SQl.</li>
<li>Sqoop metastore is a tool for using hosts in a shared metadata repository. Multiple users and remote users execute saved jobs in metastore.</li>
</ul>
</div>
</div>
<div class="ImageContent" style="text-align: justify;">
<div class="hddn"><img fetchpriority="high" decoding="async" class="alignnone size-medium aligncenter" src="https://cdn.wikitechy.com/interview-questions/sqoop/difference-between-sqoop-and-pig.png" alt=" difference between sqoop and pig" width="595" height="336" /></div>
</div>
<p style="text-align: justify;" align="center">
<div class="ImageContent">
<div class="hddn" style="text-align: justify;"></div>
</div>
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		<title>What is staging table in sqoop ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Thu, 22 Jul 2021 06:53:48 +0000</pubDate>
				<category><![CDATA[Sqoop]]></category>
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					<description><![CDATA[Answer : Staging table is used to stage the exported data...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="staging-table-in-sqoop" class="color-purple" style="text-align: justify;">Staging table in sqoop:</h2>
</div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn"></div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>Staging table is used to stage the exported data.</li>
<li>Sqoop breaks down method into multiple transactions. A failing job may create partial knowledge.</li>
<li>This could result in job failing because it could cause duplication of data or even collision while insertion.</li>
<li>Staging tables are simple database tables that contain business details.</li>
<li>Staging table acts as an auxiliary table that&#8217;s used to transferred data.</li>
<li>Sqoop breaks down export method into multiple transactions, it&#8217;s possible that a failing export job may lead to partial data being committed to the database</li>
</ul>
</div>
</div>
<div class="text-center row" style="text-align: justify;"></div>
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		<title>What is update command in sqoop ?</title>
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		<pubDate>Thu, 22 Jul 2021 06:52:07 +0000</pubDate>
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					<description><![CDATA[Answer : There are two approaches to update in sqoop...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="update-command-in-sqoop" class="color-purple">Update command in sqoop:</h2>
</div>
</div>
<div class="row">
<div class="col-sm-12">
<div class="Content">
<div class="hddn">
<ul>
<li>There are two approaches to update in sqoop.
<ul>
<li>The records inserted are to be added to the target table.</li>
<li>The sqoop provides an update mode by providing “update-key &lt;column(s)&gt;” command.</li>
</ul>
</li>
<li>This command is used to generate the SQl update statement.</li>
<li>If the value in column 1 matches the data is updated else the data is inserted into the table.</li>
<li>This technique is also refered to as upsert where the default action is update but insertion takes place when matching data is not available.</li>
</ul>
</div>
</div>
<div class="CodeContent">
<div class="hddn">
<figure class="highlight"><div class="code-embed-wrapper"> <div class="code-embed-infos"> </div> <pre class="language-markdown code-embed-pre line-numbers"  data-start="1" data-line-offset="0"><code class="language-markdown code-embed-code">$ sqoop export (Generic Arguments)<br/>  --table target-relational-table <br/>  --update-key column1<br/>  --export-dir /user/my-hdfs-file<br/>  ...<br/>Generates =&gt; UPDATE target-relational-table SET<br/>                    column2=1000,column3=2000<br/>		          WHERE column1=100;</code></pre> </div></figure>
</div>
</div>
</div>
</div>
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		<title>What is the use of sqoop ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Thu, 22 Jul 2021 06:49:42 +0000</pubDate>
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					<description><![CDATA[Answer : Sqoop is a simple tool that is used for moving data...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="sqoop-uses" class="color-purple">Sqoop uses:</h2>
</div>
</div>
<div class="Content">
<div class="hddn">
<ul>
<li>Sqoop is a simple tool that is used for moving data.</li>
<li>It is generally used move data from non hadoop data stores.</li>
<li>These non hadoop data stores are the relational databases and data warehouses .</li>
<li>Sqoop allows the users to specify the target (i.e) the place where the data is to be moved inside of hadoop from relational databases like Oracle,Teredata.</li>
<li>Data ingestion is important in any big data project because the volume of data is generally in petabytes or exabytes.</li>
<li>Hadoop <b>Sqoop </b>and Hadoop Flume are the two tools in Hadoop which is <b>used</b> to gather data from different sources and load them into HDFS.</li>
</ul>
</div>
</div>
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<div class="hddn"><img decoding="async" class="alignnone size-medium aligncenter" src="https://cdn.wikitechy.com/interview-questions/sqoop/overview-of-sqoop-metastore.png" alt="Overview of sqoop metastore" width="796" height="466" /></div>
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		<title>What is sqoop metastore ?</title>
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		<pubDate>Thu, 22 Jul 2021 06:47:16 +0000</pubDate>
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					<description><![CDATA[Answer : Sqoop metastore is an internally implemented database that can hold the definition of a given job...]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="sqoop-metastore" class="color-purple" style="text-align: justify;">Sqoop Metastore:</h2>
</div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>Sqoop metastore is an internally implemented database that can hold the definition of a given job.</li>
<li>It includes details like which database,which table,which approach,what query to extract from,what target to write to and so on.</li>
<li>Consider an array of SQL databases that have the same structure, but are partitioned by for performance reasons.</li>
<li>Thus we have the same sqoop job running against multiple database instances at the same time, all unified at a single table in Hadoop.</li>
<li>In order to get parallelization, we generate a job that manages the Sqoop sub-workflows. This results in 500 distinct sqoop jobs.</li>
<li>Metastore is used to handle these complexities.</li>
</ul>
</div>
</div>
<div class="ImageContent">
<div class="hddn" style="text-align: justify;"><img decoding="async" class="alignnone size-medium" src="https://cdn.wikitechy.com/interview-questions/sqoop/overview-of-sqoop-metastore.png" alt="overview of sqoop metastore" width="796" height="466" /></div>
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		<title>What is sqoop ?</title>
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		<pubDate>Thu, 22 Jul 2021 06:22:24 +0000</pubDate>
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					<description><![CDATA[Answer : Apache Sqoop is efficiently transferring data between Relational databases and Apache Hadoop.]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="sqoop" class="color-purple">Sqoop:</h2>
</div>
</div>
<div class="Content">
<div class="hddn"><img loading="lazy" decoding="async" class="alignnone size-medium aligncenter" src="https://cdn.wikitechy.com/interview-questions/sqoop/sqoop-tutorial.gif" alt="Sqoop Tutorial" width="743" height="414" /></div>
</div>
<div class="ImageContent">
<div class="hddn"></div>
</div>
<div class="Content">
<div class="hddn">
<ul>
<li>Apache Sqoop is efficiently transferring data between Relational databases and Apache Hadoop.</li>
<li>It is used to import data from relational databases such as MySQL, Oracle to Hadoop HDFS, and export from Hadoop file system to relational databases.</li>
<li>It improves the efficiency of data analysis by combining structured data and unstructured data.</li>
<li>Sqoop stands for SQL for Hadoop.SQL + HADOOP =SQOOP</li>
</ul>
</div>
</div>
<div class="text-center row">
<div class="col-sm-12"></div>
</div>
<div class="ImageContent"><img loading="lazy" decoding="async" class="aligncenter size-medium" src="https://cdn.wikitechy.com/interview-questions/sqoop/overview-of-sqoop-diagram.png" alt="Overview Of Sqoop Diagram" width="600" height="280" /></p>
<div class="hddn"></div>
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		<title>What is the purpose of @outputSchema decorator in Python UDF when using in Apache Pig ?</title>
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		<pubDate>Mon, 12 Jul 2021 08:49:19 +0000</pubDate>
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					<description><![CDATA[Answer:A UDF has input and output. Here is the different ways you can specify the output format of a Python UDF through use of the outputSchema decorator.]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="specifying-the-udf-output-schema" class="color-green" style="text-align: justify;">Specifying the UDF output schema</h2>
</div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>A UDF has input and output. Here is the different ways you can specify the output format of a Python UDF through use of the outputSchema decorator.</li>
</ul>
</div>
</div>
<div class="TextHeading" style="text-align: justify;">
<div class="hddn">
<h2 id="sample-code" class="color-green">Sample Code:</h2>
<div class="code-embed-wrapper"> <div class="code-embed-infos"> </div> <pre class="language-javascript code-embed-pre line-numbers"  data-start="1" data-line-offset="0"><code class="language-javascript code-embed-code"># the original udf<br/># it returns a single chararray (that&#039;s PigLatin for String)<br/>@outputSchema(&#039;word:chararray&#039;)<br/>def hi_world():<br/>    return &quot;hello world&quot;<br/>    <br/># this one returns a Python tuple. Pig recognises the first element <br/># of the tuple as a chararray like before, and the next one as a <br/># long (a kind of integer)<br/>@outputSchema(&quot;word:chararray,number:long&quot;)<br/>def hi_everyone():<br/>  return &quot;hi there&quot;, 15<br/><br/>#we can use outputSchema to define nested schemas too, here is a bag of tuples<br/>@outputSchema(&#039;some_bag:bag{t:(field_1:chararray, field_2:int)}&#039;)<br/>def bag_udf():<br/>    return [<br/>        (&#039;hi&#039;,1000),<br/>        (&#039;there&#039;,2000),<br/>        (&#039;bill&#039;,0)<br/>    ]<br/><br/>#and here is a map<br/>@outputSchema(&#039;something_nice:map[]&#039;)<br/>def my_map_maker():<br/>    return {&quot;a&quot;:&quot;b&quot;, &quot;c&quot;:&quot;d&quot;, &quot;e&quot;,&quot;f&quot;}</code></pre> </div>
</div>
</div>
<p style="text-align: justify;">OutputSchema can be used to imply that a function outputs one or a combination of basic types. Those types are:</p>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>chararray: like a string</li>
<li>bytearray: a bunch of bytes in a row. Like a string but not as human friendly</li>
<li>long: long integer</li>
<li>int: normal integer</li>
<li>double: floating point number</li>
<li>datetime</li>
<li>boolean</li>
<li>No schema is specified;then the Pig assumes that the UDF outputs a bytearray.</li>
</ul>
</div>
</div>
<div class="hddn" style="text-align: justify;"><img loading="lazy" decoding="async" class="aligncenter size-medium" src="https://cdn.wikitechy.com/interview-questions/apache-pig/apache-pig-udf.png" alt="Apache Pig UDF" width="700" height="476" /></div>
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		<title>What is the main difference between pig vs hive vs sql ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:44:28 +0000</pubDate>
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					<description><![CDATA[Answer:Pig does not have a dedicated metadata database. Hive makes use of the exact variation of dedicated SQL-DDL language by defining tables beforehand. 14. It supports Avro file format.]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-pig-vs-hive-vs-sql" class="color-green">Difference between pig vs hive vs sql</h2>
</div>
</div>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>Pig</th>
<th>Hive</th>
<th>Sql</th>
</tr>
<tr>
<td class="text-leftalign">Apache Pig also allows developers<br />
to follow multiple query approach,<br />
which reduces the data scan<br />
iterations.</td>
<td class="text-leftalign">Hive statements are similar to<br />
Structured Query Language (SQL)<br />
and limitations of Hive Query<br />
Language(HQL) in terms of<br />
the commands that it<br />
understands, it is still very useful.</td>
<td class="text-leftalign">Structured Query Language has been a programmer’s companion for decades. It was the real solution for extractingdata for further processing.</td>
</tr>
<tr>
<td class="text-leftalign">PigLatin is procedural to a<br />
large extent.</td>
<td class="text-leftalign">Hive is built with an analytical focus.</td>
<td class="text-leftalign">Structured Query Language is Informative.</td>
</tr>
<tr>
<td class="text-leftalign">Pig requires that programmers<br />
to learn something on top of<br />
structured query language(SQL).</td>
<td class="text-leftalign">Hive provides an open source<br />
implementation of MapReduce.</td>
<td class="text-leftalign">Structured Query Language demand of storing data<br />
in a strict relational database<br />
schemas and its declarative nature often deflects<br />
focus from the ultimate purpose to extract data for analysis.</td>
</tr>
<tr>
<td class="text-leftalign">It offers a number of advantages<br />
in terms of declaring execution plans<br />
Extract, transform, load(ETL)<br />
routines and pipeline modification.</td>
<td class="text-leftalign">Hive lacks update and delete<br />
functions but is superfast in reading<br />
and processing huge volumes of data<br />
faster than SQL.</td>
<td class="text-leftalign">The DBMS systems that SQL operates on,<br />
are considered to be faster than MapReduce.</td>
</tr>
</tbody>
</table>
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		<title>What is the difference between Pig, Hive and HBase ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:40:35 +0000</pubDate>
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					<description><![CDATA[Answer:It is used for semi structured data.	,Hive is query engine,HBase is a data storage particularly for unstructured data.]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-pig-hive-and-hbase" class="color-green">Difference between Pig, Hive and HBase</h2>
</div>
</div>
<div class="row">
<div class="col-sm-12">
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>Pig</th>
<th>Hive</th>
<th>Hbase</th>
</tr>
<tr>
<td class="text-leftalign">It is used for semi structured data.</td>
<td class="text-leftalign">Hive is query engine</td>
<td class="text-leftalign">HBase is a data storage particularly for unstructured data.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component is generally<br />
used by Researchers and Programmers.</td>
<td class="text-leftalign">Apache Hive is mainly used for<br />
batch processing i.e. OLAP<br />
and creating reports.</td>
<td class="text-leftalign">HBase is extensively used for transactional processing where in the response time of the query is not highly interactive i.e. OLTP</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component operates on<br />
the client side of any cluster.</td>
<td class="text-leftalign">Hive Hadoop Component operates on<br />
the server side of any cluster.</td>
<td class="text-leftalign">Operations in HBase are run in real-time on the database</td>
</tr>
<tr>
<td class="text-leftalign">Avro supported for Pig.</td>
<td class="text-leftalign">Hive does not support Avro.</td>
<td class="text-leftalign">The client which is reading/writing the data has to deal with the avro schemas, after HBase delivered the raw data to it.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop is a great ETL tool<br />
for big data because of its powerful<br />
transformation and processing<br />
capabilities.</td>
<td class="text-leftalign">Hive Hadoop Component is helpful<br />
for ETL.</td>
<td class="text-leftalign">Hbase Component is helpful for ETL.</td>
</tr>
<tr>
<td class="text-leftalign">Pig are high-level languages that<br />
compile to MapReduce.</td>
<td class="text-leftalign">Hive is also a high-level languages<br />
that compile to MapReduce.</td>
<td class="text-leftalign">HBase allows Hadoop to support the transactions on key value pairs.</td>
</tr>
<tr>
<td class="text-leftalign">Pig is also SQL-like but varies to<br />
a great extent and thus it will<br />
take some time efforts to master Pig.</td>
<td class="text-leftalign">Hive directly leverages SQL expertise<br />
and thus can be learnt easily.</td>
<td class="text-leftalign">HBase allows you to do quick random versus scan all of data sequentially, do insert/update/delete from middle, and not just add/append.</td>
</tr>
</tbody>
</table>
<div class="text-center row">
<div class="col-sm-12">
<div id="bsa-zone_1590522538159-8_123456"></div>
</div>
</div>
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</div>
</div>
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		<title>What is the difference between Pig and Sqoop in Hadoop ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:36:31 +0000</pubDate>
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					<description><![CDATA[Answer: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.]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-pig-and-sqoop-in-hadoop" class="color-green">Difference between Pig and Sqoop in Hadoop</h2>
</div>
</div>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>Pig</th>
<th>Sqoop</th>
</tr>
<tr>
<td class="text-leftalign">Apache Pig is a tool for analytics which is used to<br />
analyze data stored in HDFS.</td>
<td class="text-leftalign">Apache Sqoop is a tool to importing structured data from RDBMS to HDFS or exporting data from HDFS to RDBMS.</td>
</tr>
<tr>
<td class="text-leftalign">We can import the data from Sql databases into<br />
hive rather than NoSql Databases.</td>
<td class="text-leftalign">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</td>
</tr>
<tr>
<td class="text-leftalign">Pig can be used for following purposes ETL<br />
data pipeline, Research on raw data.</td>
<td class="text-leftalign">Important Sqoop control commands to import RDBMS data are Append, Columns and Where</td>
</tr>
<tr>
<td class="text-leftalign">The pig Metastore stores all info about the tables.<br />
And we can execute spark sql queries because spark<br />
can interact with pig Metastore.</td>
<td class="text-leftalign">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.</td>
</tr>
<tr>
<td class="text-leftalign">The scalar data types in pig are int, float,<br />
double, long, chararray, and bytearray. The complex data<br />
types in Pig are map, tuple, and bag.</td>
<td class="text-leftalign">It basically converts CHAR(x), VARCHAR(x), NUMERIC(x,y) to string (with lengh 32767), and it converts DATETIME to BIGINT.</td>
</tr>
</tbody>
</table>
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		<title>What is the difference between Pig, Hive and MapReduce ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:26:57 +0000</pubDate>
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					<description><![CDATA[Answer:Pig is a scripting language,SQL like query language,It is a compiled language]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-pig-hive-and-mapreduce" class="color-green">Difference between Pig, Hive and MapReduce</h2>
</div>
</div>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>Pig</th>
<th>Hive</th>
<th>Hadoop mapreduce</th>
</tr>
<tr>
<td class="text-leftalign">Pig is a scripting language</td>
<td class="text-leftalign">SQL like query language</td>
<td class="text-leftalign">It is a compiled language</td>
</tr>
<tr>
<td class="text-leftalign">Higher level of abstraction</td>
<td class="text-leftalign">Higher level of abstraction</td>
<td class="text-leftalign">Hadoop mapreduce used lower level of abstraction</td>
</tr>
<tr>
<td class="text-leftalign">Comparatively less line of codes<br />
than mapreduce</td>
<td class="text-leftalign">Comparatively less lines of code<br />
than mapreduce and apache pig</td>
<td class="text-leftalign">More lines of code</td>
</tr>
<tr>
<td class="text-leftalign">Development effort is less code efficiency<br />
is relatively less</td>
<td class="text-leftalign">Development effort is less code<br />
efficiency is relatively less</td>
<td class="text-leftalign">More development , effort is involved</td>
</tr>
<tr>
<td class="text-leftalign">Code efficiency is relatively less</td>
<td class="text-leftalign">Code efficiency is relatively less</td>
<td class="text-leftalign">Code efficiency is high when compared<br />
to pig and hive</td>
</tr>
<tr>
<td class="text-leftalign">Pig is open source</td>
<td class="text-leftalign">Hive open source</td>
<td class="text-leftalign">Hadoop MapReduce were built so that hadoop developers could do the same<br />
thing in Java in a less verbose way by writing only<br />
fewer lines of code that is easy to understand</td>
</tr>
<tr>
<td class="text-leftalign">Disadvantage of pig is that commands<br />
are not executed unless either you dump<br />
or store an intermediate or final result.This<br />
increases the iteration between debug and<br />
resolving the issue.</td>
<td class="text-leftalign">Disadvantages of hive is that No<br />
real time access to data, Updating<br />
data is complicated</td>
<td class="text-leftalign">The only drawback that developers need to write several lines of basic java code</td>
</tr>
</tbody>
</table>
<div class="text-center row">
<div class="col-sm-12">
<div id="bsa-zone_1590522538159-8_123456"></div>
</div>
</div>
<div class="ImageContent">
<div class="hddn"><img loading="lazy" decoding="async" class="aligncenter size-medium" src="https://cdn.wikitechy.com/interview-questions/apache-pig/differences-between-hive-and-pig.png" alt="differences-between-hive-and-pig" width="700" height="478" /></div>
</div>
]]></content:encoded>
					
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		<item>
		<title>What is the difference between Pig and Hive ?</title>
		<link>https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-and-hive/</link>
					<comments>https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-and-hive/#respond</comments>
		
		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:22:29 +0000</pubDate>
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					<description><![CDATA[Answer:Pig Hadoop Component is generally
used by Researchers and Programmers.	Hive Hadoop Component is mainly used by data analysts.]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-pig-and-hive" class="color-green">Difference between pig and Hive</h2>
</div>
</div>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>Pig</th>
<th>Hive</th>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component is generally<br />
used by Researchers and Programmers.</td>
<td class="text-leftalign">Hive Hadoop Component is mainly used by data analysts.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component is used<br />
for semi structured data.</td>
<td class="text-leftalign">Hive Hadoop Component is used for completely structured Data.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component has a procedural data<br />
flow language (Pig Latin).</td>
<td class="text-leftalign">Hive Hadoop Component has a declarative Structured Query Language (HiveQL).</td>
</tr>
<tr>
<td class="text-leftalign">Pig can&#8217;t start reliance based server.</td>
<td class="text-leftalign">Hive can start reliance based server,send queries from any nook and corner directly to the Hive<br />
server which will execute them.</td>
</tr>
<tr>
<td class="text-leftalign">Pig is also SQL like but it varies from great extent<br />
and it will take some time efforts to master Pig.</td>
<td class="text-leftalign">Hive directly borrowed SQL expertise it learnt easily.</td>
</tr>
<tr>
<td class="text-leftalign">Avro supported for Pig.</td>
<td class="text-leftalign">Avro does not support for Hive.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component operates on<br />
the client side of any group.</td>
<td class="text-leftalign">Hive Hadoop Component operates on the server side of any group.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component is highly<br />
used for programming.</td>
<td class="text-leftalign">Hive Hadoop Component is mainly used for generating reports.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop is a great ETL tool for<br />
big data because of its powerful<br />
transformation and processing capabilities.</td>
<td class="text-leftalign">Hive Hadoop Component is helpful for ETL(Extract, Transform and Load).</td>
</tr>
<tr>
<td class="text-leftalign">In Pig there is no dedicated metadata database<br />
and the schemas or data types will<br />
be defined in the script itself.</td>
<td class="text-leftalign">Hive makes use of exact variation of the SQL DLL language by defining the tables beforehand and<br />
storing the schema details in any local database.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop component does not have<br />
any notion for partitions though might<br />
be one can achieve this through filters.</td>
<td class="text-leftalign">The Hive Hadoop component has a provision for<br />
partitions so that you can process the subset<br />
of data by date or in an alphabetical order.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Hadoop Component renders users with sample<br />
data for each scenario and each step through<br />
its “Illustrate” function.</td>
<td class="text-leftalign">This feature is not incorporated with the<br />
Hive Hadoop Component.</td>
</tr>
</tbody>
</table>
]]></content:encoded>
					
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		<item>
		<title>What is the difference between Pig,Hive and Hadoop ?</title>
		<link>https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-pig-hive-and-hadoop/</link>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:18:28 +0000</pubDate>
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					<description><![CDATA[Answer:Apache Pig is a high-level,Apache Hive is a data warehouse
software project,Open-source software framework]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-pighive-and-hadoop" class="color-green">Difference between Pig,Hive and Hadoop</h2>
</div>
</div>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr>
<th>Pig</th>
<th>Hive</th>
<th>Hadoop</th>
</tr>
<tr>
<td class="text-leftalign">Apache Pig is a high-level<br />
platform for creating programs<br />
it runs on Apache Hadoop.</td>
<td class="text-leftalign">Apache Hive is a data warehouse<br />
software project built on top of<br />
Apache Hadoop for providing<br />
data query, and<br />
analysis.</td>
<td class="text-leftalign">Open-source software framework for distributed storage and processing dataset of big data using the MapReduce programming model.</td>
</tr>
<tr>
<td class="text-leftalign">Pig Latin extracts the programming<br />
from the Java MapReduce phrase<br />
into a code which creates<br />
MapReduce programming high level.</td>
<td class="text-leftalign">Hive provides the SQL<br />
abstraction to integrate<br />
SQL-like queries (HiveQL).<br />
The underlying Java without<br />
we need to implement queries in the<br />
low-level Java API.</td>
<td class="text-leftalign">The Hadoop Common package for Java ARchive (JAR) files and scripts needed to start Hadoop.</td>
</tr>
<tr>
<td class="text-leftalign">Pig basically has 2 parts:<br />
the Pig Interpreter and the<br />
language, PigLatin</td>
<td class="text-leftalign">It provides us data warehousing<br />
facilities on top of an<br />
existing Hadoop cluster.</td>
<td class="text-leftalign">Hadoop is basically two things:Hadoop Distributed File System (HDFS) +a Computation or Processing framework (MapReduce).</td>
</tr>
<tr>
<td class="text-leftalign">Pig makes our life a lot easier,<br />
otherwise writing MapReduce is<br />
always not easy.</td>
<td class="text-leftalign">User can create tables in Hive and<br />
store data there. Along with<br />
that user can even map their<br />
existing HBase tables to Hive<br />
and operate on them.</td>
<td class="text-leftalign">Hadoop is a distributed, scalable,big data store, modelled after Google&#8217;s BigTable.It stores data as key/value pairs.</td>
</tr>
</tbody>
</table>
]]></content:encoded>
					
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		<title>What is the difference between group and cogroup in Pig Latin ?</title>
		<link>https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-difference-between-group-and-cogroup-in-pig-latin/</link>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:13:18 +0000</pubDate>
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					<description><![CDATA[Answer:For readability GROUP is used Cogroup used as a statements]]></description>
										<content:encoded><![CDATA[<div class="row">
<div class="col-sm-12">
<div class="TextHeading">
<div class="hddn">
<h2 id="difference-between-group-and-cogroup-in-pig-latin" class="color-green" style="text-align: justify;">Difference between group and cogroup in Pig Latin</h2>
</div>
</div>
<table class="table-bordered table-striped table table-responsive">
<tbody>
<tr style="text-align: justify;">
<th>Group</th>
<th>Cogroup</th>
</tr>
<tr style="text-align: justify;">
<td class="text-leftalign">For readability GROUP is used<br />
in statements involving one relation</td>
<td class="text-leftalign">Cogroup used as a statements involving two or more relations. Cogroup should no exceed 127 relations at a time.</td>
</tr>
<tr style="text-align: justify;">
<td class="text-leftalign">The group statement collects together<br />
records with the same key</td>
<td class="text-leftalign">cogroup is a general statement of group. Instead of collecting records of 1 input it collects records of N inputs based on a key</td>
</tr>
<tr>
<td class="text-leftalign" style="text-align: justify;">Syntax:<br />
grunt&gt; Group_data = GROUP Relation_name BY age;</td>
<td class="text-leftalign" style="text-align: justify;">Syntax:<br />
alias = CROSS alias, alias [, alias …] [PARTITION BY partitioner] [PARALLEL n];</td>
</tr>
</tbody>
</table>
</div>
</div>
]]></content:encoded>
					
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		<title>What is the best tool to process web streaming data in Hadoop,PIG or HIVE ?</title>
		<link>https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-best-tool-to-process-web-streaming-data-in-hadoop-pig-or-hive/</link>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:07:43 +0000</pubDate>
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		<guid isPermaLink="false">https://www.wikitechy.com/interview-questions/?p=199</guid>

					<description><![CDATA[Answer:It contains easy programming]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="best-tool-to-process-web-streaming-data-in-hadoop-or-pig-or-hive" class="color-green" style="text-align: justify;">Best tool to process web streaming data in Hadoop or PIG or HIVE :</h2>
</div>
</div>
<div class="Content">
<div class="hddn">
<ul>
<li style="text-align: justify;">It contains easy programming.</li>
<li style="text-align: justify;">It is little to achieve parallel execution of simple, “embarrassingly parallel” data analysis tasks.</li>
<li style="text-align: justify;">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.</li>
<li style="text-align: justify;">To upgrade their execution automatically, allowing the user to focus on semantics rather than efficiency and Extensibility.</li>
<li style="text-align: justify;">A special-purpose processing to create own functions.</li>
<li style="text-align: justify;">For efficient working pig and hive can be used together.</li>
<li style="text-align: justify;">Pig is best tool for parsing (ETL) kind of job, and even pig supports UDF better than hive.</li>
<li style="text-align: justify;">It can help you to develop you own framework using pig where you can call Hive ql and map-reduce job for better functionality.</li>
</ul>
</div>
</div>
]]></content:encoded>
					
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		<title>What is the advantages of pig in Hadoop ?</title>
		<link>https://www.wikitechy.com/interview-questions/apache-pig/what-is-the-advantages-of-pig-in-hadoop/</link>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 08:04:01 +0000</pubDate>
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		<guid isPermaLink="false">https://www.wikitechy.com/interview-questions/?p=197</guid>

					<description><![CDATA[Answer:The development time is Decrease....]]></description>
										<content:encoded><![CDATA[<div class="TextHeading">
<div class="hddn">
<h2 id="advantages-of-pig-in-hadoop" class="color-green" style="text-align: justify;">Advantages of pig in Hadoop</h2>
</div>
</div>
<div class="Content">
<div class="hddn">
<ul style="text-align: justify;">
<li style="list-style-type: none;">
<ul>
<li>The development time is Decrease.</li>
<li>It is a collection of Rich set of operators in order to perform operations such as join, filer, sort and many more.</li>
<li>Pig Latin is similar to SQL, it becomes very easy for developers to write a Pig script.</li>
<li>The task execution in Apache Pig it gets automatically optimized , hence the programmers need to focus on semantics language.</li>
</ul>
</li>
</ul>
<div class="ImageContent" style="text-align: justify;">
<div class="hddn"></div>
<div><img loading="lazy" decoding="async" class="aligncenter size-medium" src="https://cdn.wikitechy.com/interview-questions/apache-pig/big-data sdd.gif" alt="Big data sdd" width="589" height="360" /></div>
</div>
<ul>
<li style="text-align: justify;">Using the existing operators, users can easily develop their own functions to process,read, and write data.</li>
<li style="text-align: justify;">Pig provide the facility for creating User Defined Functions UDF’s, we can easily create a number of programming languages such as Java and invoke them in Pig Scripts.</li>
<li style="text-align: justify;">All types of data handling are having both structured &amp; unstructured is provided by Apache Pig and the results are stored inside HDFS.</li>
<li style="text-align: justify;">You have UDFs which you want to parallellize and utilize for large amounts of data, then you are in luck. Use Pig as a base pipeline where it does the hard work and you just apply your UDF in the step that you want.</li>
</ul>
</div>
</div>
]]></content:encoded>
					
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		<title>What is Hbase used for, and what are the benefits that it provides over Pig and Hive ?</title>
		<link>https://www.wikitechy.com/interview-questions/apache-pig/what-is-hbase-used-for-and-what-are-the-benefits-that-it-provides-over-pig-and-hive/</link>
					<comments>https://www.wikitechy.com/interview-questions/apache-pig/what-is-hbase-used-for-and-what-are-the-benefits-that-it-provides-over-pig-and-hive/#respond</comments>
		
		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 07:58:18 +0000</pubDate>
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					<description><![CDATA[Answer:HBASE will not replace Map Reduce. It is scalable distributed database....]]></description>
										<content:encoded><![CDATA[<div class="row">
<div class="col-sm-12">
<div class="TextHeading">
<div class="hddn">
<h2 id="hbase-uses" class="color-green" style="text-align: justify;">Hbase uses</h2>
</div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>HBASE will not replace Map Reduce. It is scalable distributed database.</li>
<li>Hbase processing data will act as Map Reduce.</li>
<li>The Hadoop database is define Apache HBase.</li>
<li>HBase is a sub-project of the Apache Hadoop project and is used to provide real-time read and write access to big data.</li>
<li>Hbase is a Bigtable clone.</li>
<li>Hbase is a open source.</li>
<li>Hbase called as Cascading connector.</li>
</ul>
</div>
</div>
<div class="text-center row" style="text-align: justify;">
<div class="col-sm-12">
<div id="bsa-zone_1590522538159-8_123456"></div>
</div>
</div>
<div class="ImageContent" style="text-align: justify;">
<div class="hddn"></div>
<div><img loading="lazy" decoding="async" class="aligncenter size-medium" src="https://cdn.wikitechy.com/interview-questions/apache-pig/hbase-layout.png" width="550" height="397" /></div>
</div>
<div class="TextHeading" style="text-align: justify;">
<div class="hddn">
<h2 id="what-are-the-features-that-it-provides-over-pig-and-hive" class="color-green">what are the Features that it provides over Pig and Hive?</h2>
</div>
</div>
<div class="Content" style="text-align: justify;">
<div class="hddn">
<ul>
<li>Possible to create tables in Hive and store data,it can even map the user’s existing HBase tables to Hive and operate on them.</li>
<li>Hive should be used for analyzing query of data collected over a period of time.</li>
<li>Hbase has got its own set of very good Application Programming Interface(API) which could be used to push/pull the data.</li>
<li>It has a good community for the future.It developed on top if hadoop already used to integration for the Hadoop platform.</li>
<li>Hbase can be integrated with MapReduce that can do bulk operation, like indexing, analytics etc.
<ul>
<li>Distributed storage</li>
<li>Table-like in data structure</li>
<li>multi-dimensional map</li>
<li>High scalability</li>
<li>High availability</li>
<li>High performance</li>
</ul>
</li>
</ul>
</div>
</div>
<div class="ImageContent" style="text-align: justify;">
<div class="hddn"><img loading="lazy" decoding="async" class="aligncenter size-medium" src="https://cdn.wikitechy.com/interview-questions/apache-pig/benefits-that-it-provides-over-pig-and-hive.png" width="959" height="431" /></div>
</div>
<div class="Content">
<div class="hddn">
<ul>
<li style="text-align: justify;">A distributed data store that can scale horizontally to 1,000s of commodity servers and petabytes of indexed storage.</li>
<li style="text-align: justify;">Designed to operate on top of the Hadoop distributed file system (HDFS) or Kosmos File System (KFS, aka Cloudstore) for scalability, fault tolerance, and high availability.</li>
</ul>
</div>
</div>
</div>
</div>
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		<title>What is default numbers of reducers while executing a pig query ?</title>
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		<dc:creator><![CDATA[Editor]]></dc:creator>
		<pubDate>Mon, 12 Jul 2021 07:14:15 +0000</pubDate>
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					<description><![CDATA[Answer:Where XXX is the number of reducer.]]></description>
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<h2 id="set-number-of-reducer-in-pig" class="color-green">Set Number of Reducer in Pig:</h2>
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<figure class="highlight"><div class="code-embed-wrapper"> <div class="code-embed-infos"> </div> <pre class="language-markdown code-embed-pre line-numbers"  data-start="1" data-line-offset="0"><code class="language-markdown code-embed-code">SET default_parallel XXX</code></pre> </div></figure>
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<li>Where XXX is the number of reducer.</li>
<li>This command is used to set the number of reducers at the script level</li>
<li>The coder need to write this configuration at top/beginning of their pig script.</li>
<li>Alternatively, use the PARALLEL clause to set the number of reducers at the operator level.</li>
<li>We set the value using the PARALLEL clause will override any value we specify through (“SET default parallel.”) to reduce phase you can include the PARALLEL clause with any operator.</li>
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<li>COGROUP</li>
<li>CROSS</li>
<li>DISTINCT</li>
<li>GROUP</li>
<li>JOIN (inner)</li>
<li>JOIN (outer) and</li>
<li>ORDER BY.</li>
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<h2 id="for-example" class="color-green">For Example:</h2>
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<p>In GROUP operator the PARALLEL class has been used.</p>
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<li>A = LOAD &#8216;myfile&#8217; AS (t, u, v);</li>
<li>B = GROUP A BY t PARALLEL 18</li>
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<p>Here 18 is number of reducer.</p>
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<li>If neither “set default parallel” nor the PARALLEL clause are used, using size of the input data Pig sets the number of reducers.</li>
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<p>The properties values has been specified</p>
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<li><code>pig.exec.reducers.bytes.per.reducer</code> &#8211; Defines the number of input bytes per reduce; Pig reducer default value is 1000*1000*1000 (1GB).</li>
<li><code>pig.exec.reducers.max</code> &#8211; Defines the upper bound on the number of reducers; Pig reducer default is 999.</li>
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