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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...

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Assignment 3: Practical and Written Assessment

Assessment Task:
This is anindividualassessment.

In this assessment, you are required to produce a report based on the Big Data strategy document you developed for Assessment-2(Presentation). You also need to analyse the datasets relevant to the business that you identified in Assessment 1 using any big data tools and describe how the outputs of these tools could help you to create the Big Data Strategy.You can include any additional datasets that would support your big data strategy.

At the beginning of the report, you will identify some Big Data use cases based on the Big Data strategies you developed for Assessment 2. In the following part, you will critically analyse different Big Data technologies, data models, processing architectures and query languages and discuss the strengths and limitations of each of them. You will also discuss different Big Data analytics and business intelligence tools that can be applied on the chosen datasets so businesses can gain actionable insights from Big Data. Moreover, you will discuss the Big Data technologies that you could use for data collection, storage, transformation, processing and analysis to support your use cases.

You will also illustrate the Big Data technology stack and processing architecture required to support your use cases. You have to provide the rationale behind each of the choices you make. Finally, you will specify what user experiences you are going to provide to aid in decision-making. Your target audience is executive business people who have extensive business experience but limited ICT knowledge. Hence, they would like to be informed as to how new Big Data technologies that you have applied on the datasets could benefit their business. Please note that a standard report structure, including an executive summary, must be adhered to.

The main body of the report should include but not limited to the following topics:

1. Big Data Use Cases

2. Critical Analysis of Big Data Technologies

3. Big Data Architecture Solution

The length of the report should be around 3000 words. You are required to do an extensive reading of more than 10 articles relevant to the chosen Big Data use cases, technologies, architectures and data models. You will need to provide in-text referencing of the chosen articles. You assessment must have a Cover page (Student name, Student Id, Unit Id, Campus, Lecturer and Tutor name) and Table of Content (this should be MS word generated).

Caution:ALL assessments will be checked for plagiarism by Turnitin.
Assessment Submission:
You must upload the written report to Moodle as a Microsoft Office Word file by the above due date.
Assessment Criteria:

You will be assessed based on your ability to critically analyse, use and evaluate different Big Data technologies and to apply Big Data architecture, tools, and technologies to support Big Data use cases. The marking criteria for this assessment are as follows.

Executive Summary - 3 marks

Table of Contents - 2 marks

Introduction - 2 marks

Big Data Use Cases - 3 marks

Critical Analysis of Big Data Technologies - 8 marks

Use of Big Data tools on the dataset - 5 marks

Critical analysis on the output - 8 marks

Big Data Architecture Solution - 3 marks

Conclusion - 3 marks

References - 3 marks

Answered Same Day May 29, 2021 COIT20253 Central Queensland University


Kuldeep answered on Jun 09 2021
143 Votes
Big Data
Big Data
Topic: Big Data Analysis
Student Name:
Unit Name:
University Name:
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
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
eak every morning, however unlike other data mining technologies, how does big data provide business intelligence? How is it uique from running SQL queries and
owsing 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
eaches. 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
oader, 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
ought 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
owse 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...

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