Project final

College of Computing and Informatics

2020/2021 Second Semester

Course Code	DS520
Course Name	Big Data Processing and Analytics
CRN
Assignment type	Critical Thinking Project
Module	All modules
Assignment Points	10

Student ID
Student Name

Project Template

Task 1:

1.1 Literature Review:

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1.2 References:

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Task 2:

2.1 Introduction

Provide a short description of your project and an overview about the data you are analysing.

2.2 Body section

2.2.1 Data

This section should include a description of the data being analyse (include number of samples in the dataset, features and their types, descriptive statistics of the data, etc).

2.2.2 Steps:

In this section, write the steps and commands you used to import the data and.

Task 3:

3.1 MapReduce Algorithm (Comment your Code)

Write the complete code you applied.

3.2 Results

Include a written description of the statistical results, and its meaning based on the dataset you have chosen.

Task 4:

4.1 Steps:

In this section, write the steps and commands you used to import the data and.

Task 5:

5.1 Applied Queries on MongoDB

Write the complete code you applied with describing the function of each query.

5.2 Results

Include a written description of the results. Discuss the meaning of the results based on the data set.

Task 6:

6.1 Applied Code on Hive/Pig

Write the complete code you applied with describing the function of each query.

6.2 Results

Include a written description of the results. Discuss the meaning of the results based on the data set.

Task 7:

6.1 Applied Code on SparkSQL

Write the complete code you applied with describing the function of each query.

6.2 Results

Include a written description of the results. Discuss the meaning of the results based on the data set (Include visualization of the results) Figures must be added.

Task 8:

8.1 Applied Code on Spark (Using MLib)

Write the complete code you applied with describing the machine learning algorithm and why you choose it.

8.2 Results

Include a written description of the results. Discuss the meaning of the results based on the data set.

Conclusion

Restate the main results of your analysis and provide any future recommendations.