Pig Vs MapReduce Vs Sql

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Apache Pig

MapReduce

Apache Pig is a data flow language.

MapReduce is a data processing paradigm.

It is a high level language.

MapReduce is low level and rigid.

Performing a Join operation in Apache Pig is pretty simple.

It is quite difficult in MapReduce to perform a Join operation between datasets.

Any novice programmer with a basic knowledge of SQL can work conveniently with Apache Pig.

Exposure to Java is must to work with MapReduce.

Apache Pig uses multi-query approach, thereby reducing the length of the codes to a great extent.

MapReduce will require almost 20 times more the number of lines to perform the same task.

There is no need for compilation. On execution, every Apache Pig operator is converted internally into a MapReduce job.

MapReduce jobs have a long compilation process.

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·                   Apache Pig Vs SQL

·         Listed below are the major differences between Apache Pig and SQL.

Pig

SQL

Pig Latin is a procedural language.

SQL is a declarative language.

In Apache Pig, schema is optional. We can store data without designing a schema (values are stored as $01, $02 etc.)

Schema is mandatory in SQL.

The data model in Apache Pig is nested relational.

The data model used in SQL is flat relational.

Apache Pig provides limited opportunity for Query optimization.

There is more opportunity for query optimization in SQL.

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