Technologies to Analyze Big Data

  • Hassan, Ruman Ul

Currently, the majority of the firms like Facebook, Google, and Amazon are generating an intensive data which data is referred to as big data. As well as the above mentioned options, there are many other sources like banking, airlines, currency markets, and digital marketing that generates big data. Nandimath, Patil, Banerjee, Kakade, and Vaidya (2013) declare that the quantity of data being made daily is increasing speedily and the size of this data is nearer to zeta bytes (p. 700). This implies how big is the info is increasing quickly. This data contains a value that can benefits business organizations to boost their business stability and increase their earnings. However, this big data creates the situation of safe-keeping and processing. Prior to ten years before, the data was stored and prepared in a normal databases management system. This system is called as Relational Repository Management System (RDBMS). After the climb of big data, it is very difficult for the RDBMS to process this large data. Thus, many researchers focuse their analysis in developing a technology that can effectively review the big data.

After comprehensive research, Google has proposed a google file system for keeping the big data and a map reduce algorithm for processing this data. In addition, Nandimath et al. (2013) assert that Apache hadoop can be used for distributed handling of big data (p. 700). This framework helps many organizations in proficiently analyzing their big data. Beside Hadoop, the other systems that help in analyzing the best data are Pig, Hive, Hbase, Zoo Keeper, and Sqoop. Each tool has their own requirements, so the usage of these tools will depend on the criticality of the data and the requirement of the business or business. However, the three major technology to investigate big data are hadoop, hive, and pig.

Hadoop is one the major solutions to analyze the top data. It is the framework produced by Apache for handling extensive data collections. This framework helps business firms to effectively process their unstructured data like video recording, music and image. Furthermore, this framework benefits many business organizations to improve their financial steadiness by effectively studying their data. Furthermore, the hadoop construction contains two main components, hadoop sent out document system (HDFS) and map reduce development paradigm. The function of HDFS is to store the complete datasets in sent out environment. Allocated environment allows the developer to store the large data units on multiple machines. Thus, it helps in improving the retrieval procedure for immense data. Furthermore, Nandimath et al. (2013) state that "Hadoop uses its record system HDFS which facilitates fast transfer of data which can sustain node failure a complete" (p. 700). In addition, it helps developer to overcome the storage space problem. For example, if enormous data is stored about the same machine then it generates a problem of control and retrieving due to its size. Thus, if that data is allocated on multiple machines then it offer an decrease for the programmer for handling and retrieving. Beside fast processing and retrieving, trustworthiness is also a good thing about HDFS. HDFS achieve high consistency by replicating the info on different machines. Therefore, if any machine fails in allocated environment, then the data of this particular machine will be easily retrieved through backups.

According to Dittrich and Ruiz (2012), the benefit of map reduce is the fact coders need to define only solo functions for map and reduce task (p. 2014). This map reduce paradigm helps builders to overcome the challenge of efficiently handling the data. In addition, Nandimath et al. (2013) believe that the purpose of map is to divide the work into smaller parts and deliver it to different nodes, as the reason for reduce is to create the desired final result (p. 701). For instance, if Facebook needs to analyze an individual interest then your Facebook will first deploy the generated data on HDFS and performs the map process to separate the zeta byte of data and then perform the reduce job to get the required consequence. Thus, it implies that hadoop helps organizations for efficiently analyzing their extensive datasets.

Another technology to analyze big data is hive. It is a data warehouse construction build after hadoop. It provides an potential for the developer to composition and analyze the data. In hadoop, the info processing task is conducted using Java program writing language while in hive, handling a task is performed using organised query terminology (SQL). Furthermore. Borkar, Carey, and Liu (2012) assert that "Hive is SQL-inspired and reported to be used for over 90% of the Facebook map reduce use cases" (p. 2). Thus, the primary goal of hive is to process the data through SQL like program. Moreover, the traditional SQL requirements were restricting the hive from accomplishing some intensive functions like extracting, changing and loading the top data. As a result, hive developed their own query language called hive query dialect (HQL).

Besides traditional SQL expectations, HQL includes some specific hive extensions that provide an convenience for the designer to effectively review the best data. Furthermore, hive helps builder to triumph over the scalability issue by using distributed file system system. In addition, it helps them to attain the fast response time through HQL. For example, general SQL assertions like SELECT and Put will consume more time on traditional databases management system for big data while in hive the same businesses can be performed efficiently. Furthermore, Liu, Liu, Liu, and Li (2013) conclude that with precise system parameter tuning in hive, a satisfactory performance can be achieved (p. 45). This means if the builder precisely changes the machine parameters for studying the info, then performance efficiency can be upgraded for that activity.

Besides hadoop and hive, pig is also a major technology to investigate the big data. Pig allows the builder to investigate and process the great datasets quickly and easily through transformation. Additionally it is called dataflow dialect. The pig framework is utilized along with HDFS and map reduce paradigm. The working of pig is similar to that of hive except the query dialect. In pig an activity is performed using pig latin whereas in hive, the task is performed using HQL. The main benefit of pig is the fact pig latin concerns can be integrated with other languages like Java, Jruby, and Python and it also allow users to establish their own functions to execute the task according to their needs. Additionally, as pig is a dataflow dialect it helps builder to illustrate the info transformation process. For example, in pig it is not hard to perform the data transformation operations like Break up, Stream, and Group compare to SQL. Furthermore, the pig framework is divided into two parts pig latin words and pig interpreter. The pig latin is a query terms to process big data. Furthermore, Lee, Lee, Choi, Chung, and Moon (2011) assert that in pig framework an activity is prepared using pig latin terminology (p. 14). The pig latin inquiries help developer to process the data efficiently and quickly. Another component of pig platform is pig interpreter. The work of interpreter is to convert the pig latin concerns into map reduce careers and to evaluate the pests in pig latin queries. For instance, if Facebook designer creates the pig latin query to find the people in India that like rock music, then this query is first interpreted by pig interpreter to recognize pests and then it is converted to map reduce careers. Thus, by making use of pig latin inquiries, designers can avoid the strain of writing a tedious code in java to execute the same action.

In conclusion, the three systems to process the big data are hadoop, hive, and pig. These frameworks help business organizations to get the value off their data. In addition, each technology is useful for performing an activity differently. For example, Apache Hadoop pays to for examining the offline data and it cannot process the real time data like bank data. In addition, hive offers a SQL like software that makes the processing easier because the user does not have to create the lengthy wearisome code. Hive is wonderful for those user who aren't good at coding and best in SQL. In the same way, pig also makes the processing task much easier for users. All the map reduce careers can be written in pig latin questions to get desired results. Therefore, organizations should choose the technology based on their data types and requirements. However, all these systems help organizations to process and store their data effectively.

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