Maya management technologies have recently begun to

MayaPelichetProfessorLloyd ConstantCPST331026November 2017Big Data & Hadoop: Transformingthe Data Architecture of Peter Mayer  IntroductionData storage and managementtechnologies have recently begun to surge in popularity. Businesses want tolearn how to implement the best ways to store, maintain, capitalize on the copiousamounts of data that their products, consumers, services, etc. generate. Withthat being said, organizing and measuring data has proven to be quite difficultdespite present-day technological innovations.

The term “ Big data” has emergedand Apache Hadoop, or Hadoop, technology uses a set of algorithms to processlarge clusters of data (Kelly, 2014). This report serves as documentationof research conducted on the benefits and barriers of Apache Hadoop as well asa proposal to the management of Peter Mayer Advertising to implement the ApacheHadoop platform to restructure the advertising agency’s big data. About HadoopApache Hadoop is an open-sourcecomputing platform that stores and processes data. It includes a storage systemknown as Hadoop distributed file system (HDFS). HDFS is capable of storing largeamounts of data, growing incrementally, and surviving failure of major parts ofstorage infrastructure (Dowling).

Hadoop influences a cluster—a set of looselyor tightly connected computers—of hubs to run MapReduce programs. The MapReduceprogramming model is comprised of two steps: the “ Map” procedure organizesinformation and the “ Reduce” procedure assembles the transitional results intoa final result, or summary operation. (Dowling) Each single cluster node—aspecial type of computational cluster for storing data—node has a neighborhoodrecord framework and CPU to MapReduce programs on. Data is broken into pieces, stored over the local records of hubs, and then copied. The local records form arecord framework called the Hadoop Distributed File System (HDFS) (Lay, 2010).

Hadoop has developed into adecision support system for researchers, data scientists, informationarchitects, etc. that have to analyze information in fields that generatecopious amounts of data ( i. e. education, finance, public relations, andadvertising). The shifting nature of data itself has also spiked interest innew approaches to data collection, especially concerning technologies that havethe capacity to parse the information from social media and mobile devices(Mone, 2013).

Benefits & Barriers of Implementing Hadoop AtPeter MayerBelow is an examination of thebenefits and barriers of implementing Hadoop to restructure the big data ofPeter Mayer Advertising to increase the agency’s network security, overallprofit, and consumer satisfaction. Barriers of Implementing Hadoop at Peter MayerData MisidentificationLeveraging the value of data can beextremely complicated. If the data at Peter Mayer is misidentified, thendetermining the best ways to go about using the agency’s big data could proveto be very ambiguous and or difficult to articulate. Workforce AvailabilityQualified professionals that areable work on new technologies and to interpret data are limited.

“ Big Data” isa relatively new concept, consequently, there is a shortage of experts that caninterpret the information of a business like Peter Mayer’s to get understandingof what the agency’s needs are. Data Access and ConnectivityMany institutions, businesses, organizations, companies, etc.  lack thecorrect technologies and software to manage and aggregate their data. Whilethere are organizations that are working to providing lasting solutions, thisis a problem that could hinder the implementation of Hadoop at Peter MayerAdvertising. Changing Technical PatternsTechnical patterns that exist indata industry are constantly changing. Innovative employees, business partners, and leaders are needed to develop the right information technologyinfrastructure for a specific entity (business, institution, corporation, etc.

)that will cooperate with the industry’s changing landscape. Collaboration EffortsLeveraging big data requirescross-functionality in fields such as engineering, IT, and finance and theability to determine where the owners of a business observe data fragmentationin the organization. Collaborating all functions of the business could provedifficult to implement.  Lack of Data Protection Lastly, data protection is a roadblock thatconstantly hinders organizations from taking total advantages of their data, especially given the frequency in which data breaches occur. Benefits of Implementing Hadoop at Peter MayerHadoop’s data architecture helpsstreamline massive amounts of big data. Three of the most popular types of bigdata that are collected are clickstream data, sentiment data, and server logdata. While similar, each of these types of big data can provide very differentvalue to Peter Mayer. Below is a proposal to the management of Peter Mayerabout the aforementioned types of big data and how implementing Hadoop to makeits big more valuable (Kelly 2014).

Clickstream DataClickstream data is used tounderstand how website visitors research and consider purchasing products. Withclickstream analysis, Peter Mayer can optimize its websites and promotionalcontent to improve the likelihood that visitors and customers will learn aboutthe performance of the advertising the agency produces. A record of these behaviorpatterns would assist the digital marketing team at Peter Mayer with judging theeffectiveness of different types of advertisements—with the confidence thattheir results are statistically meaningful and reproducible (“ ClickStreamData Analysis”). Hadoop Makes Clickstream Data More ValuableTools like Omniture and GoogleAnalytics already help digital teams at Advertising businesses like Peter Mayeranalyze clickstreams. However, Hadoop adds three key benefits to theclickstream data of Peter Mayer. First, Hadoop can join clickstream data withother data sources like CRM (customer relationship management data) customerdemographics data, and information its advertising campaigns. This additionaldata can provide Peter Mayer with a more comprehensive evaluation of how theinformation can be used as opposed to an isolated analysis of clickstream alone(“ Analyzing Click Stream Data Using Hadoop”). Secondly, Hadoop scales easily thatyears of data can be stored without incremental costs, allowing institutions toperform temporal or year to year analyses of their clickstream data.

PeterMayer Advertising could save years of data and to discover deeper patterns inthe clickstream that its competitors have missed. Finally, Hadoop makes websiteanalysis easier. Without Hadoop, clickstream data is very difficult to processand structure. With Hadoop, inexperienced business analysts and data scientistscan use Apache Hive or Apache Pig scripts to organize clickstream data. Hadoopmakes storing and refining the data easy, so that analysts of varyingexperience can focus on the discovery of data patterns (“ ClickStream DataAnalysis”). Sentiment DataSentiment data is unstructured dataon opinions, emotions, and attitudes that is contained in sources like socialmedia posts, blogs, online product reviews and customer support interactions. Businesses use sentiment analysis to understand how the public feels about a specifictopic and tracks how those opinions change over time.

Peter Mayer can analyzesentiment about their advertisements to gauge public reaction to the work itproduces for its clients. Hadoop Stores Sentiment Data With Hadoop, social media posts canbe loaded into the Hadoop Distributed Files System (HDFS) using Apache Flumefor real-time streaming. Apache Pig and Apache Mahout organize the unstructureddata and score sentiment with advanced machine learning methodologies. Sentiment analysis quantifies subjectiveviews expressed by consumers and target audiences on social media. Researchers needbig data to do this reliably. Words and phrases are assigned a polarity scoreof positive, neutral or negative. By scoring and aggregating millions ofinteractions, analysts can judge candid sentiment at scale, in real time.

After scoring sentiment amongst a targetaudience, Peter Mayer can combine this social media data with other sources ofdata. Clickstream data can be used in conjunction with sentiment data toattribute what was previously anonymous sentiment to a particular type ofcustomer and or segment of customers. The results from this conjunction of datacan be visualized and used to improve the advertisements that Peter Mayer makesthat affect their target customer and or segment of customers (McKenna 2015). Server Log DataLarge corporations businesses like PeterMayer usually build, manage and protect their information networks. Server logsare computer-generated records that report data on the status and operations ofthese networks. The volume of server logs is typically massive, and often timesthese logs are insignificant.

However, the two of the most commonuse cases for server log data are network security breaches and networkcompliance audits. In both of these cases, server log information is vital forboth rapid, efficient problem resolution and long-term resource planning. Hadoop Helps You Protect Network SecurityHigh-profile data breaches happenfrequently.

Enterprises and government agencies invest vast sums on antivirusand firewall software to protect their networks from malware and outsideattacks, and those solutions usually work. When security fails, Hadoop helps businesslike Peter Mayer understand and repair its vulnerabilities quickly and facilitatesroot cause analysis to create lasting protection (“ Security Think Tank”).  Conclusion            The report contains three maintopics: information about Hadoop, the benefits implementing Hadoop at PeterMayer and the barriers of implementing Hadoop at Peter Mayer.  It also serves as documentation of researchand a thorough proposal to the management of Peter Mayer Advertising toimplement Hadoop to restructure the agency’s big data. While there are some barriersthat could make the implementation of Hadoop at Peter Mayer somewhat difficult, the pros of implementing Hadoop outweigh the cons. Peter Mayer would experiencea significant capital gain by investing in this technological tool.