Pig v/s Hive v/s SQL v/s MapReduce
Let’s discuss about the Pig v/s Hive v/s SQL v/s MapReduce.
Let’s see the differences between pig and hive.
|Pig uses a language called Pig Latin.||Hive uses a language called HiveQL.|
|Pig Latin is a data flow language.||HiveQL is a query processing language.|
|It was originally created at Yahoo.||It was originally created at Facebook.|
|Apache Pig can handle structured, unstructured, and semi-structured data.||Basically Hive handle only structured data.|
|It is generally used by Researchers and Programmers.||Hive is used mainly by data analysts.|
|It is mainly used for programming.||It is mainly used for creating reports.|
|This component operates on the client side of any cluster.||This component operates on the server side of any cluster.|
|Pig supports Avro.||It does not support.|
Let’s see the differences between pig and SQL.
|Pig Latin is a procedural language.||SQL is a declarative language.|
|Nested relational data model is used in pig.||flat relational data model used in SQL.|
|Here schema is optional.||Here Schema is mandatory.|
|It provides limited opportunity for Query optimization.||It provides more opportunity for query optimization.|
Let’s see the differences between pig and MapReduce.
|It is a data flow language.||It is a data processing paradigm.|
|Pig is a high level language.||MapReduce is low level language.|
|Here Performing a Join operation is simple.||Here performing join between datasets is quite difficult.|
|Basic knowledge of SQL is enough to work conveniently with Apache Pig.||Exposure to Java is must to work with MapReduce.|
|It uses multi-query approach to decrese the length of the codes.||Here length of the code is very high.|
|In pig there is no need for compilation,|
because for every execution pig operators internally converted to MapReduce job.
|MapReduce jobs have a long compilation process.|
“That’s all about the comparison of Pig v/s hive v/s SQL v/s Mapreduce, acoording to our project requirement we can choose any tool in bigdata Hadoop”.