Big Data Assignment

This repository contains materials and code for a Big Data assignment.
Use this README as a template and adapt it to your specific task and technology stack.

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Overview

• Course / context: Fill in course name or project context here.
• Objective: Briefly describe the main goal of the assignment (e.g., process large datasets, build ETL pipeline, run analytics, etc.).
• Technologies: List main tools here, e.g. Hadoop, Spark, Kafka, Python, Scala, Jupyter, SQL, etc.

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Project Structure

Update this section as you add files and folders.

• README.md: Project description, setup and usage instructions.
• Add more entries here as your project grows, for example:
  • data/ – raw and processed datasets (usually excluded from version control).
  • notebooks/ – exploratory analysis or prototype code.
  • src/ – main application or job code.
  • scripts/ – helper scripts for running jobs or managing data.

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Setup

Describe how to prepare the environment needed to run your assignment.
Adjust or replace the items below to match your stack.

• Prerequisites

  • Install a recent version of Python, Java/Scala, or other required languages.
  • Install big-data frameworks you use (e.g. Spark, Hadoop, Kafka) or ensure access to a cluster.
  • Install any needed package managers (e.g. pip, conda, maven, sbt, npm).

• Environment setup

  • If using Python: create a virtual environment and install dependencies from requirements.txt (once it exists).
  • If using Scala/Java: run mvn install or sbt compile (when build files are added).
  • If using Docker: document which images and docker-compose commands to run.

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Data

Document where the data comes from and how to obtain it.

• Source: Describe dataset source (provided by instructor, public dataset URL, etc.).
• Location: Explain where to put data files in this repository (e.g. data/raw/, data/processed/).
• Size / format: Mention approximate size and formats (CSV, Parquet, JSON, etc.).
• Privacy / ethics: Note any restrictions or anonymization requirements if applicable.

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How to Run

Explain how to execute your jobs, scripts, or notebooks.

• Basic steps (example, adjust as needed)

  1. Prepare the environment (see Setup).
  2. Download or place data into the correct folder.
  3. Run the main job or notebook, for example:
     • python src/main.py
     • spark-submit src/job.py
     • spark-submit --class MainClass target/app.jar
     • Open notebooks/analysis.ipynb in Jupyter and run all cells.

• Configuration

  • Document configuration files or environment variables (e.g. config.yml, .env).
  • Describe how to set paths for input/output data, cluster addresses, etc.

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Results & Evaluation

Summarize how you evaluate success and where to find results.

• Outputs: Describe generated outputs (tables, charts, reports, models, logs, etc.).
• Metrics: List key metrics (e.g. runtime, throughput, accuracy, error rates).
• Reports: Link to any final report or presentation once available.

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Development Notes

Use this section to capture important implementation details or decisions.

• Assumptions: List major assumptions about data, infrastructure, or APIs.
• Limitations: Note any known limitations or trade-offs.
• Future work: Ideas for improvement or extension of the assignment.

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License / Academic Integrity

• License: Add license information here if required (e.g. MIT, proprietary, none).
• Academic integrity: If this is coursework, follow your institution's policies on collaboration and code sharing.