Apache Pig supports many built-in operators that provide support for data operations like ordering, joins, filters, sorting, etc.Thus, it helps in quick development by reducing the development time by almost 16 times. This reduces the length of the code by 20 times. Pig uses a multi-query approach, that means by using a single Pig Latin query, we can perform multiple MapReduce tasks.Without writing the complex Java implementations in MapReduce, the programmers can easily achieve the same implementations by using Pig Latin. It uses a language called ‘Pig Latin’ which is a high-level data flow language.For the programmers, Apache Pig is a savior because: So in order to provide flexibility to the programmers and reduce the burden of writing long MapReduce code, Apache Pig was developed. Also, even if someone has good hands on Java, then also they feel a headache while writing MapReduce jobs.Īt an approx 10 lines of Apache Pig code is equal to the 200 lines of MapReduce code. In such a scenario, Apache Pig comes out as a boon for the programmers who are not good with programming languages like Java or Python.
To perform a simple task, we have to write 200 lines of codes. Previously, for analyzing data in Apache Hadoop, we had to write MapReduce jobs in Java, which is not an easy task.
0 kommentar(er)
