Hive, originally developed by Facebook and later owned by Apache, is a data storage system that was developed with a purpose to analyze organized data. Working under an open-source data platform called Hadoop, Apache Hive is an application system that was released in the year 2010 (October).
Introduced to facilitate fault-tolerant analysis of hefty data on a regular basis, Hive has been used in big data analytics and has been popular in the realm for more than a decade now.
Even though it has many competitors like Impala, Apache Hive stands apart from the rest of the systems due to its fault-tolerant nature in the process of data analysis and interpretation.
Apache Hive is a particularly efficient tool when it comes to big data (exponential data that is to be analyzed). A warehouse data software that supports the data analysis process of big data on a regular basis, the concept of hive big data is quite popular in the technological realm.
As data is stored in the Apache Hadoop Distributed File System (HDFS) wherein data is organized and structured, Apache Hive helps in processing this data and analyzing it producing data-driven patterns and trends. Fit to be used by organizations or institutions, Apache Hive is extremely helpful in big data and its ever-changing growth.
The concept of Structured Query Language or SQL software is involved in the process which communicates with numerous databases and collects the required data. Understanding Hive big data through the lens of data analytics can help us get more insights into the working of Apache Hive.
By using a batch processing sequence, Hive generates data analytics in a much easier and organized form that also requires less time as compared to traditional tools. HiveQL is a language similar to SQL that interacts with the Hive database across various organizations and analyses necessary data in a structured format.
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Hive in big data is a milestone innovation that has eventually led to data analysis on a large scale. Big organizations need big data to record the information that is collected over the time.
To produce data-driven analysis, organizations gather data and use such software applications to analyze their data. This data, with Apache Hive, can be used for reading, writing, and managing information that has been stored in an organized form. Ever since data analytics has come into being, storage of data has been a trending topic.
Even though small scale organizations were able to manage medium-sized data and analyze it with traditional data analytics tools, big data could not be managed with such applications and so, there was a dire need for advanced software.
As data collection became a daily task and organizations expanded in all aspects, data collection became exponential and vast. Furthermore, data began to be dealt in petabytes that define storage of vast data.
For this, organizations needed hefty equipment and perhaps that is the reason why the release of a software like Apache Hive was necessary. Thus, Apache Hive was released with the purpose of analyzing big data and producing data-driven analogies.
Here are 2 case studies of airbnb and theguardian that can help you to understand the use of Hive in Big Data.
"Airbnb connects people with places to stay and things to do around the world with 2.9 million hosts listed, supporting 800k nightly stays. Airbnb uses Amazon EMR to run Apache Hive on a S3 data lake. Running Hive on the EMR clusters enables Airbnb analysts to perform ad hoc SQL queries on data stored in the S3 data lake. By migrating to a S3 data lake, Airbnb reduced expenses, can now do cost attribution, and increased the speed of Apache Spark jobs by three times their original speed."
"Guardian gives 27 million members the security they deserve through insurance and wealth management products and services. Guardian uses Amazon EMR to run Apache Hive on a S3 data lake. Apache Hive is used for batch processing. The S3 data lake fuels Guardian Direct, a digital platform that allows consumers to research and purchase both Guardian products and third party products in the insurance sector." (big-data)
Hive in Big Data is extremely beneficial. While it has its own cons, the pros of Hive make it an unbeatable option available for data optimization and analysis.
The USP of Apache Hive can be summed up in its benefits that have been highly helpful in big data analysis over the time. Here are a few benefits that will make you understand the concept better.
Hive in Big Data is an easy-to-use software application that lets one analyze large-scale data through the batch processing technique. An efficient program, it uses a familiar software that uses HiveQL, a language that is very similar to SQL- structured query language used for interaction with databases.
Such a software can be operated by both programmers and non-programmers, making it a very accessible and easy-to-use application for converting petabytes of data into useful data strands.
This is one of the biggest benefits of Apache Hive that has made it a popular choice for data analytics among large organizations with vast data.
The technique of batch processing refers to the analysis of data in bits and parts that are later clubbed together. Moreover, the analyzed data is sent to Apache Hadoop, while the schemas or derived stereotypes remain with Apache Hive.
The technique of batch processing makes Apache Hive a fast software that conducts the analysis of data in a rapid manner. In addition, Apache Hive is an advanced data analysis batch processing software that is unlike traditional tools.
Thus, this particular software can handle big loads of data in one go as opposed to the traditional softwares that could only filter moderate-sized data in one go.
In most of the softwares that is used to handle Big Data today, fault tolerance is a rare feature. However, Apache Hive and the HDFS file system together work in a fault-tolerant manner that operates on the basis of replica creation.
This means that as soon as big data is analyzed in Hive, it is immediately replicated to other machines. This is done in order to prevent loss of data or schemas just in case a particular machine fails to work or stops operating.
Fault tolerance in Hadoop (Hive) is one of the biggest benefits of Hive as it beats other competitors like Impala and makes Hive unique in its own way.
Another reason why Apache Hive is beneficial is that it is a comparatively cheaper option. For large organizations, profit is the key. Yet with technologically advanced tools and softwares that are expensive to operate, profit margins can stoop low.
Therefore, it is necessary for organizations to look out for cheaper options that can help them achieve the same goals but with cost-effective measures. When it comes to big data and data analysis, Apache Hive is one of the best softwares to use and operate.
Fast and familiar, it is highly efficient and also relies on fault tolerance to produce better results.
Apache Hive is a productive software. Why? Well, the answer lies in its other benefits. Apache Hive not only analyzes data, but also enables its users to read and write the data in an organized manner.
What's more is that this software defines specific schemas related to data analysis and stores them in Hadoop Distributed File System (HDFS) which helps in future analysis.
Henceforth, Hive in Big Data is quite productive and enables large organizations to make the best use of the data collected and generated over a long period of time to convert it into meaningful bits and pieces.
(Must check: Big Data Analytics Tools)
Hive in Big Data is eventually diminishing in terms of its value. With more and more cloud softwares like Google Bigquery that are more efficient in terms of instant tracking of data, Apache Hive is taking a back seat with gradual deterioration of its brand in the market.
The future of Hive in big data predictions does not seem too bright, yet it still is one of the leading softwares of its own time. As the contemporary big data is more elastic in terms of its distribution, Hive is a slightly slower process as compared to others.
With many scholars and technology leaders declaring Apache Hive 'dead', the future of the software can be summed up as a declining journey.
To sum up, Apache Hive was launched in October 2010 with an aim to facilitate data analysis of big data available across organizations. Fast and familiar, efficient and reliable, Hive emerged to be one of the best big data software tools of its time.
Even though the future of the software does not look much promising, it has surely been a star in driving big data analysis to its peak in the past decade. With more and more competitors coming up, the software still stands unique in terms of its features that are highly appreciated.
Big Data is going nowhere and so, more advanced versions of Apache Hive is what the technological field requires today in order to deal with vast amounts of petabytes of data being generated every second.
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