Big Data Analytics (Research Paper Sample)

Big Data Analytics
Name
Institutional Affiliation
Big data
The big data concept has been around for decades. Many firms now comprehend that if all data is captured that streams to the firm, they could apply analytics and attain strong value. Big data analytics benefits are efficiency and speed. A few years ago, a firm would have obtained information, run analytics, and extracted information that could have been utilized for future decisions. That firm can currently identify visions for immediate decisions. The capability to stay agile and work faster offers firms a competitive edge they never had before. Big data analytics aids firms harness their data and utilize it to identify novel opportunities. That, in turn, aids in smarter firm moves, higher profits, more efficient operations, and happier customers.
Big data analytics depicts the complex procedure of examining big data to unearth information like market trends, correlations, hidden patterns, and customer preferences that can aid forms to make informed business choices. On a broader scale, data analytics techniques and technologies offer a means to evaluate data sets and take away novel information to help firms make informed business choices (Addo-Tenkorang & Helo, 2016). Business intelligence inquiries answer basic questions regarding business performance and operations. Big data analytics is a kind of advanced analytics that comprises complex applications with statistical algorithms, predictive models, and what-if evaluation powered by analytics systems.
Big data analytics via specialized software and systems could lead to positive firm-related outcomes like better customer service, more effective marketing, improved operational efficiency, new revenue opportunities, and more competitive advantages over competitors. Big data analytics applications permit data scientists, data analytics, statisticians, predictive modelers, and other analytics specialists to evaluate growing volumes of structured transaction data, together with other kinds of data that are normally left untapped by analytics programs and conventional business intelligence. That comprises a mix of unstructured and semi-structured data. For instance, web server logs, internet clickstream data, social media content, mobile phone records, survey responses and text from customer emails, and machine data attained via sensors linked to the internet of things (Ahmed et al., 2017).
In some cases, NoSQL and Hadoop clusters systems are utilized primarily as staging areas and landing pads for data. That is before getting it loaded into an analytical database or data warehouse for analysis, normally in a summarized kind that is more conductive to unique structures. However, big data analytics clients adopt the Hadoop data lake concept that serves as the key repository for incoming raw data streams. Big data has become progressively valuable in supply chain analytics. Big supply chain analytics use big data and quantitative methods to improve decision-making procedures across the supply chain (Duan, & Xiong, 2015). Precisely, big supply chain analytics increases datasets for enhanced analysis beyond conventional internal data found on supply chain administration and enterprise resource planning systems.
Additionally, big supply chain analytics execute highly effective statistical techniques on existing and new data sources. The insights attained facilitate more effective and better-informed decisions that improve and benefit the supply chain. Another significant benefit of big data analytics for firms is data quality. The data quality software could conduct enrichment and cleaning of large data sets by using parallel processing (Gao, Koronios, & Selle, 2015). Those software are extensively utilized for getting reliable and consistent outputs from the processing of big data.
Hadoop
Huge datasets processing paradigm has been shifted to distributed architecture from centralized architecture. As the firm faced issues of gathering massive chunks of data, they realized that the data could never be processed utilizing any of the current centralized architecture solutions. The firms faced efficiency issues, time constraints, the elevated cost of infrastructure with the data processing and performance in the centralized environment. These huge firms overcame those issues of extracting pertinent information from a massive data dump with a distributed architecture. A key tool utilized in the market for harnessing the distributed architecture to solve the data processing issue is Apache Hadoop (Wamba et al., 2017). Utilizing Hadoop's several components like map-reduce algorithms, data clusters, and distributed processing, issues like location-founded complex data issues will be solved and offer the pertinent information back to the system, thus mounting the user experience.
Hadoop depicts the batch processing unit for a cluster of nodes that offers the underpinning of numerous big data analytics aspects as it bundles two functionality sets most required to deal with massive unstructured datasets, namely the Hadoop distributed file system and the MapReduce. MapReduce depicts the parallel programming method for writing distributed applications developed at Google for efficient large data processing on massive product hardware clusters in a fault-tolerant, reliable manner. The Map-Reduce program work son the Hadoop that is an Apache open-source concept. The Hadoop distributed file system is founded on the google file structure and offers a distributed file structure developed to run on commodity hardware (Youssra & Sara, 2018). It possesses numerous similarities with current distributed file systems. However, the disparate from other distributed file structures are vital. It is hugely fault-tolerant and is developed to be positioned on low-cost hardware. It offers high throughput admission to application data and is appropriate for applications having massive datasets.
It is quite costly to build massive servers with heavy arrangements that handle massive scale processing. Still, as a substitute, one can tie together numerous commodity computers with singular-CPU as a solitary functional distributed system. Basically, the clustered machine could read the dataset in parallel and offer a much-increased throughput. Additionally, it is cheaper compared to one high-end server. So that is the first motivational aspect behind utilizing Hadoop that it runs across low-cost and clustered machines. Hadoop runs code through a cluster of the computer. That procedure comprises the following key tasks that Hadoop performs. Data is primarily divided into files and directories. Files are allocated into a uniform blocked size of 64M and 128M (Addo-Tenkorang & Helo, 2016). These files are later distributed across several cluster nodes for additional processing. Being top on the local file system, HDFS supervises the processing. There is a replication of blocks for handling hardware failure.
Currently, Hadoop is utilized on massive amounts of data. Utilizing Hadoop, firms can harness data that was formerly difficult to analyze and manage. Hadoop is utilized by approximately 62% of firms to manage a huge amount of unstructured events and logs. Particularly, Hadoop can process enormously huge data volumes with varying structures. HDFS is utilized when the data amount is too much for a singular machine (Ahmed et al., 2017). HDFS is more intricate than other file structures, given the uncertainties and complexities of networks.
Big data depicts information collections that would have been regarded as gigantic, intolerable to process and store years back. The processing of such massive quantities of data enforces specific methods. A classic database administration system is incapable of processing as much information. Hadoop is utilized by firms with massive data volumes to process. The various companies using Hadoop comprise eBay, Facebook, LinkedIn, Amazon, and Twitter. HDFS depicts the distributed file structure offering high-performance entry to data across clusters of Hadoop (Duan, & Xiong, 2015). Hadoop ensures distributed, resilient processing of huge unstructured data sets between commodity computer clusters where every node of the cluster comprises its storage. Hadoop counts on two servers comprising taskTracker and jobTracker. Jobtracker reduces or maps tasks to organize and run their execution on the cluster.
Compare and contrast HBase and other Big Data Databases.
Hive and Hbase are two Hadoop founded big data technologies that serve divergent purposes. For example, when one login to Facebook, they see several things like the news feed, friend list, and individuals who liked your status. Since Facebook is a huge website, the billions of users using the site need to have big data technology such as Hbase or Hive or Hadoop doing the entire work at the backend. The big data systems complexity needs that every technology requires to be utilized in conjunction with each other. Hbase and Hive are both data stores for unstructured data storing. Hive is never preferably a database, but a mal-preduce founded SQL engine running on top of H