Assignment 3: Practical and Written Assessment Weighting:40% Assessment Task: This is anindividualassessment. In this assessment, you are required to produce a report based on the Big Data strategy...

Big Data
Big Data
Topic: Big Data Analysis
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Contents
Executive Summary    2
Table of Contents    2
Introduction    2
Big Data Use Cases - 3 marks    3
Critical Analysis of Big Data Technologies    5
Use of Big Data tools on the dataset    8
Critical analysis on the output    10
Big Data Architecture Solution - 3 marks    11
Conclusion    12
References    14
Executive Summary
Table of Contents
Introduction
Nowaday’s associations have large amounts of data in all factors of their operations. You may have heard analysts talking about using power of the big data during your coffee break every morning, however unlike other data mining technologies, how does big data provide business intelligence? How is it uique from running SQL queries and browsing Excel spreadsheets?
Throughout the "Big Data and Search Wars" series, we identified six powerful big data use cases as well as their impact on several companies. Analyzing big data can help companies answer key questions, test hypotheses, and ultimately improve business results. Well-managed big data also enables organizations to identify the location and spread of sensitive data and track their use so that companies can discover and respond to potential data breaches. Big data use cases include using streaming data, IoT data, and other sources to increase efficiency, analysis, and automation. Big data projects may focus on providing specific business benefits, for example, using financial transaction data for real-time fraud detection, building a 360-degree view of customer data to gain a deeper understanding of customers, or using predictive analytics to detect and replace mechanical components before failure. Or they can take the form of a broader, enterprise-wide modernization plan, such as building a centralized data lake to store all enterprise data for big data analysis, moving data to a cloud-based data lake, or migrating to a cloud data warehouse.
Big Data Use Cases - 3 marks    
Use Case #1: Log Analytics
Log data is the foundation of several business big data application. Log analysis and management tools existed long before big data appeared. However, with exponential development of the business transactions and activities, storing, processing, and displaying log data most efficiently and cost-effectively can become a huge challenge (Bettencourt, 2014). Most of the commercial as well as open-source log analysis tool can also provide you with the capability to analyze, process, or collect large amounts of log data without having to dump the information into relational database moreover retrieve it throughout SQL queries.
Use Case #2: Recommendation Engines
If you have ever used YouTube, Spotify, Netflix, and other media or social services, you might notice videos, movies or music that is "recommended for you". Does it feel great to choose only personalized choices for you? This is simple. save time. As strong competitors fill the media or entertainment space, capability to provide a top-level user experiences will be a magic weapon for victory (Bolsover and Howard, 2017).
Big data is scalable and powerful. It can handle a large amount of structured data (such as video titles searched by users, their favorite music genres) moreover know data (such as user listening / viewing patterns), which can allow industries to analyze data. One billion clicks or view data from you or other user like you to get the best advice. Over time, during ML furthermore predictive analysis, suggestions will become more suitable for users' tastes.
Use Case #3: Insurance Fraud Detection 
Associations that process huge amounts of economic transactions are continuing to look for innovative and effective ways to combat frauds. Medical insurance companies are no exclusion because frauds can cost business up to $ 6 billion a year (Herland, Khoshgoftaar and Bauder, 2018). This process can sometimes lead to long delays in official fraud cases, which can cause huge losses to businesses.
With the big data technology, billions of dollars in bills and claims records can also be processed as well as pulled into a search engine hence that investigator can examine individual report by performing an intuitive search on graphical interface.
Use Case #4: Relevancy and Retention Boost for Online Publishing
For research and publishing industries, providing the right content for their online subscribers is very critical to establishing authority, increasing subscriber base, furthermore increasing profits. Additionally to investing a lot of SEO efforts to make published website searchable, develop a strategy that once users visit the website, they can provide content well, which is the main factor affecting conversion or repeat business (Daki et al., 2017).
With the increase of personalization, a big data has brought new paradigms for analyzing and processing content data (topics, titles, authors) moreover user data (preferences, document downloads). Primary a powerful search engine can help to clean up or enrich the metadata of research document to make sure that users find a relevant content moreover easily browse associated content. Then, throughout predictive analytics and machine learning, publishers will be capable to provide content in specific order, with the favorite content of user’s appearing in top outcomes. How do they know? As they can constantly test the performance of search engines and score them offline to expect search accuracy as well as abandonment rate before they can be placed on real-time websites (Dhar, 2014). Like the cloud, a big data seems to be a buzzword, but in the next few years, big data will continue to exist and continue to enrich its business technology ecosystem.
Critical Analysis of Big Data Technologies
Over the past few decades, the latest technological advancements have led to a big amount of information from several fields. Big data contains a lot of structured, unstructured data, and semi-structured, which exceeds the processing capacity of traditional databases. In addition to the huge amount of data, big data is usually unstructured or want real-time analysis (Saheb and Saheb, 2020). The IT industry has responded by providing big data tools and technologies and methods. However, many existing methods and technologies have limitations.
Big data requirements
Big data also refers to huge data sets that are difficult to store, visualize, share, search, or analyze data. On the Internet, amount of the data we process has developed to TB or Peta
Bytes: As amount of data continues to grow, the kinds of data produced by applications are more abundant than before. As an outcome, traditionally relational databases face...