Running head: ADVANTAGES OF APACHE PIG 1

ADVANTAGES OF APACHE PIG 2

Advantages of Apache Pig

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The Apache Pig as a data flow language method developed to analyze big data in companies has advantages such as ease programming, extensibility of capabilities and the scene behind optimization codes. The Apache pig method eases programming in that it does not require high skills in map reduce jobs because the curves needed must not be steep and thus any person can participate in map-reducing. The less time required for development makes the process ease and thus creates an advantage in the vanilla map-reduce job (Sahoo et al., 2016). The programming process is not declarative thus it is simple to follow the guidelines and the commands involved in the programming this reduces the length of the code thus making the programming easy.

The extensibility of capabilities is another important advantage to Apache Pig. This feature allows an individual to develop own functions that they can use to read and analyze data basing on the existing operators. Through this extensibility, an individual can control executions since the process of analyzing the data is procedural following the commands (Shoro & Soomro, 2015). This makes the process become straightforward in developing vanilla map-reduce jobs. Apache pig’s extensibility makes the data substantial parallelization this eases makes an advantage in that it can handle huge amounts of data.

The optimization of codes in apache pig reduces the time used in analyzing the data. Through reducing code lengths of various words, the data can be handled faster and thus can analyze big data with short periods of time. Optimization of codes resulting in breaking down of data map reduces thus making the apache pig method much useful and easier to handle various data types (Fegaras & Noor, 2018). For example with the use of optimizing codes Hadoop developers no longer use their later methods which involved writing java codes to help them analyze the data.

References

Shoro, A. G., & Soomro, T. R. (2015). Big data analysis: Apache spark perspective. Global Journal of Computer Science and Technology.

Sahoo, S. S., Wei, A., Valdez, J., Wang, L., Zonjy, B., Tatsuoka, C., … & Lhatoo, S. D. (2016). Neuropigpen: A scalable toolkit for processing electrophysiological signal data in neuroscience applications using apache pig. Frontiers in neuroinformatics, 10, 18.

Fegaras, L., & Noor, M. H. (2018, July). Compile-time code generation for embedded data-intensive query languages. In 2018 IEEE International Congress on Big Data (BigData Congress) (pp. 1-8). IEEE.