|Type of paper:
|Data analysis Information technologies
Big data analytics can be defined as the analysis of big data to examine the hidden patterns, correlations, and relationships between the data variables. The modern technology applies different technology to derive significant information from the big data. There are some big data analytics tools on the market today. Organizations have resolved to make this use of these data analytics tools to uncover the new revenue sources in their area of business, determine the customers' preferences and market trends through data analysis and visualization (Sivarajah, Kamal, Irani, & Weerakkody, 2017). Some of the most popular big data analytic tools include; Spark, HBase, and Hive.
It is crucial to look at the number of variables that the datasets have during the dataset inspection procedure. By knowing the number of the variables in the data, it will make it possible for one to create suitable Hive tables under your schema to receive the data in the datasets. Although the dataset may contain a number sheets and variables, inspection of the dataset help in to determine the vital variables in each dataset. After the inspection, one should load the data into Hive for analysis. Alternatively, one can inspect the dataset after loading them in Hive.
To create the suitable Hive tables, "CREATE EXTERNAL TABLE IF NOT EXISTS" will be used. The external table in Hive does not manage storage - meaning that its deletion does not affect the data in Hive. After creating the external table, the next step will be to create an ORC table which will be managed by Hive. Finally, data from the external table will be inserted into the Hive ORC table.
There are two main methods that can be used to insert data into the hive tables. One can use DML operations or HDFS command. In this case, we will use the "Load data" Hive command to ingest all rows of each of the datasets into the target tables. Some advantages if using include some application advantages such as; the user has complete control and access to the files he stores and enhanced data retrieval techniques are available to retrieve stored data (Prakash). Additionally, the file systems can compress the files, still permitting the applications to access them (Morabito, 2015). The system automatically decompresses the file when we need them and compresses it again when the file is closed.
The LOAD DATA statement is, LOAD DATA local path "file_name" INTO TABLE table_name.the statement will help in loading data into the tables that were previously created. The data inserted in the Hive tables are formatted according to the specified table format during the creation.
Data analysis is the next step after making sure that data has been inserted into the tables. Hive Data Manipulation Language (DML) statements and summary functions are essential when it comes to manipulation of data. DML statements can be used to insert new fields into the data, delete existing data and perform numerical operations on the data. Some of the DML statements that will be of importance in generating reports are COMPUTE STATS and CREATE FUNCTION [IF NOT EXISTS].
On the other hand, Hive summary functions will play a critical role in finding some descriptive statistics summaries for some of the variables in our data. Common summary functions that must be considered are Sum, Maximum, Minimum, Average and Count. Finding these statistics is from the dataset will help in understanding some aspects of the data that could otherwise have not been identified. The reports generated will focus on determining the basic characteristics of the data being evaluated.
The first report will be that of the dataset descriptive statistics. That is; a report that depicts the sum, mean, minimum, maximum, variance, count, covariance, and correlation of the variable. However, one should keep in mind that this report will be applicable for the numerical variables only. Secondly, a report on the on the relationship between the variable should be generated. This report will solely aim at uncovering some unnoticed relationship between the datasets. To do so, Hive DML statements will come in handy.
The report summaries are produced on the rationale that the essence of big data cannot be determined without thorough data analysis and manipulation. Therefore, one must dig into the data using a data analysis tool to get some meaningful conclusions. These reports are the key to understanding some of the most important components of the datasets. Consequently, the reports will be used to make a conclusion on the datasets by exploring the outcome of the crucial variables in the datasets.
For a better understanding of large data, the data need to be analyzed represented clearly. Hive is one of the data analytics tools that have the capability of analyzing big data to uncover the hidden aspects of the data. Through reports and data summaries, Hive can help businesses and organizations to derive useful conclusions from their data. Also, data analytics enable organizations to evaluate their sales and project their expected income in the future. Therefore, organizations will need to employ data analytics specialists who can evaluate big data using the right tools, explain the results and apply them in their daily
Morabito, V. (2015). Big Data and Analytics Innovation Practices. Big Data and Analytics, 157-176. doi:10.1007/978-3-319-10665-6_8
Prakash, R. V. (n.d.). Big Data Analysis. Effective Big Data Management and Opportunities for Implementation, 83-93. doi:10.4018/978-1-5225-0182-4.ch006
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. doi:10.1016/j.jbusres.2016.08.001
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