COMP6210 Big Data Assignment 1 - MapReduce Help | Macquarie University

If you are a student at Macquarie University’s School of Computing enrolled in COMP6210 - Big Data, you already know that Assignment 1 (worth 30% of your grade) is a challenging milestone. This task tests your ability to work with MongoDB, Pymongo, and MapReduce (Mrjob) while analyzing the Olympic historical dataset (1896–2022).

Below, we unpack the tasks and share how our A+ grade assignment help provides well-commented Python, verified outputs (athletes.txt, output1_2.txt, etc.) and matching flowcharts, all delivered on time and plagiarism-free.

Understanding COMP6210 Assignment 1

Course: COMP6210 - Big Data

Assignment: 1 - MapReduce Objectives:

• Practice data curation and preprocessing using Python & MongoDB

• Implement sorting and aggregation with MapReduce

• Analyze Olympic datasets for patterns in medals, athletes, events, and countries

• Create flowcharts explaining MapReduce processes

Assignment Breakdown

Task 1 - Data Curation (20 marks)

• Task 1.1 Data Extraction (10 marks): Extract medal-winning athletes from Summer Olympics (1980–2020). Generate a text file athletes.txt in the format:

  <id, country, year, event, medal>

• Task 1.2 Data Organization (10 marks): Use MapReduce to sort entries by athlete ID in ascending order.

Task 2 - Data Analysis with MapReduce (60 marks)

Task 2.1 - Top Athletes (20 marks): Find top 3 athletes for each medal category (Gold, Silver, Bronze) between 1980–2020.

Task 2.2 - Top Countries (20 marks): Find top 3 countries with highest Gold medals. Also list Silver & Bronze counts.

Task 2.3 - Top Events by Decade (20 marks): Identify top 3 events per decade (1980s–2010s) based on total medal counts.

Task 3 - Flowcharts (20 marks)

• Prepare 3–4 page Word/PDF document with flowcharts for MapReduce programs in Task 2.

• Tools like Miro or draw.io are recommended.

• Flowcharts must match your code.

How to Approach COMP6210 Assignment 1

Step 1: Understand the Dataset

Familiarize yourself with the Olympic dataset. Pay attention to fields like year, athlete ID, country, event, and medal.

Step 2: Work with MongoDB + Pymongo

Import data into MongoDB, filter medal winners, and export entries for MapReduce.

Step 3: Use MapReduce Model Strictly

Macquarie requires all analysis (Tasks 1.2, 2.1–2.3) to be done using Mrjob MapReduce only. Other Python approaches = 0 marks.

Step 4: Document with Flowcharts

Flowcharts should clearly show Map → Shuffle → Reduce stages with sample data.

Step 5: Test Outputs

Check formatting of text files (athletes.txt, output1_2.txt, etc.) before submission.

Get Professional Help with COMP6210 Assignments

Assignments in Big Data can be complex, requiring a mix of dataset handling, Python coding and visualization. If you need help with:

• Extracting & organizing Olympic datasets

• Writing MapReduce programs in Python (Mrjob)

• Debugging MongoDB queries with Pymongo

• Creating clean & accurate flowcharts

• Submitting plagiarism-free solutions on time

Our team of experts at JavaOnlineHelp provides Macquarie University Assignment Help tailored for COMP6210 coursework.

We ensure:

✅ Plagiarism-free, original solutions

✅ Well-commented Python code (Mrjob & Pymongo)

✅ Output files + Flowchart documentation included

✅ On-time delivery before deadlines

✅ Support for undergrad & postgrad computing units

COMP6210 Assignment 1 is not just about writing Python scripts—it’s about applying Big Data principles to analyze large-scale Olympic records. With 30% of your grade at stake, professional guidance can make the difference between a Pass and an A+.

FAQ – Macquarie University COMP6210 Assignment Help

Q1. What does COMP6210 Big Data cover at Macquarie University?

COMP6210 focuses on Big Data concepts and technologies, including distributed computing, MapReduce, Hadoop, MongoDB, DASK, and large-scale data analysis. The unit gives students hands-on experience with tools and frameworks used in real-world data-driven applications.

Q2. Can I get help with COMP6210 Assignment 1?

Yes. We provide full support for COMP6210 Assignment 1 – MapReduce, including Python code, dataset curation and flowchart creation.

Q3. Are the solutions plagiarism-free?

Yes. All solutions are written from scratch, with originality checks for academic integrity.

Q4. Do you provide MongoDB & Pymongo support?

Absolutely. We guide you in using MongoDB + Studio 3T + Pymongo for extraction and dataset filtering.

Q5. How fast can I get my assignment done?

We offer deadline-focused delivery (24 hours, 48 hours, or before your due date).

Q6. Do you cover all tasks of Assignment 1?

Yes, Tasks 1 (Curation), Task 2 (Analysis) and Task 3 (Flowcharts) are fully covered.

Q7. Is online tutoring available for COMP6210?

Yes. Along with assignment help, we offer 1:1 tutoring for Big Data concepts.

We will happy to assist You:  

WhatsApp now : +91-995 3141 035 (Click here For quick response)

Solution Includes: AI plagiarism report + clean Python code with 100% accuracy.Your success is our excellence.