Analyzing Varieties of Agricultural Data Using Big Data Tools Pig

Day by day, data is growing rapidly. Analysis of the data is necessity. As per recent survey data generated in last 2 years is more than the data created in entire previous history of human. Data created in different form and in diversified manner. It can be structured, it can be semi-structured, or it can be unstructured. To analyze diversified by agricultural data we can use the tools of Big Data like Pig. Using Pig, we can analyze varieties of data. Pig is a platform for analysis of data. Biggest advantage of Pig is it can process any diversified data very quickly and allows us to use user defined functions. Use Case of Pig is ETL. It is used to extract the data from sources then after applying transformation we can load it into the data warehouse. We will do analysis of state wise proportion circulation of Numeral of operative properties for all societal collections in 2005-06 and 2010-11 using Pig. Oriental Journal of Computer Science and Technology Journal Website: www.computerscijournal.org ISSN: 0974-6471, Vol. 10, No. (4) 2017, Pg. 810-816


Introduction
Nowadays, data is growing very speedy.Analysis of the data is necessity for the many organization.As per recent survey data generated in last 2 years is more than the data created in entire previous history of human.Data created in different form and in diversified manner.It can be structured, it can be semi-structured, or it can be unstructured.To analyze diversified by agricultural data we can use the tools of Big Data like Pig.Using Pig, we can analyze varieties of data.Pig is a platform for analysis of data.Biggest advantage of Pig is it can process any diversified data very quickly and allows us to use user defined functions.Use Case of Pig is ETL.It is used to extract the data from sources then after applying transformation we can load it into the data warehouse.
Here, in this study we analyzed verities of agricultural data using the big data tools Pig.

Analysis of Structured Agricultural Data Using Pig
To analyze structured data, first we must identify the source of data.Source of structured data can be  Once retrieve the comma separated values file from government website, we copied the file on linux platform.Once we copied on linux then we moved the same file on HDFS platform.Following is command to move the file from linux root directory to HDFS directory named PARAG.CopyFromLocal command is used to move the file from linux directory to HDFS directory.hadoop fs -copyFromLocal /root/state_data.csv/ PARAG After moving the file from linux root directory to HDFS directory, we can load the data on Pig using Grunt shell Step-2 Display the loaded data We can use dump statement to display the data in Grunt Shell.

Fig.4: State wise proportion circulation of Numeral of operative properties
Step-3 Filter Specific Data For analysis of any data we can use filter or aggregate functions.Here, we are filtering the specific data from state Gujarat.Finding all state data which census_small of 2010 is more than 30

Conclusion
We did analysis of agricultural data of state wise proportion circulation of Numeral of operative properties for all societal collections in 2005-06 and 2010-11 using Pig.We analyzed structured agricultural data using Pig.As we know that day by day requirement of analysis of the data is increasing rapidly.To demonstrate the use of analysis using big data tools Pig we used the government agricultural data and did the analysis of data.
Analysis of the data is necessity for the many organization.Data created in different form and in diversified manner.It can be structured, it can be semi-structured, or it can be unstructured.To analyze diversified by agricultural data we can use the tools of Big Data like Pig.Using Pig, we can analyze varieties of data.Pig is a platform for analysis of data.Biggest advantage of Pig is it can process any diversified data very quickly and allows us to use user defined functions.Use Case of Pig is ETL.It is used to extract the data from sources then after applying transformation we can load it into the data warehouse.