Olympic Games Analytics Project in Apache Spark for beginner (Udemy.com)
Olympic Games Analytics Project in Apache Spark for beginner using Apache Zeppelin
Created by: Bigdata Engineer
Last updated February 2026
What you will learn
- Set up a free Databricks account and launch a Spark cluster for analytics projects.
- Navigate and use Apache Spark notebooks effectively for data analysis.
- Load, structure, and explore datasets using Spark DataFrames.
- Perform real-world analytics on Olympic Games data, including:
- Age, height, and weight distribution of medal-winning athletes.
- Women’s medal trends over the years.
- Top medal-winning countries and sports.
- Gold, Silver, and Bronze medal distribution analysis.
- Athlete demographics and performance patterns over time.
- Create data visualizations to present insights from Spark outputs.
- Publish Spark notebooks to the web to share project results.
- Build a portfolio-ready Spark project demonstrating end-to-end data analytics skills.
How it compares
Against the other 150 rated Udemy courses in Hadoop on CourseDuck:
- Rating4.1 out of 5higher than 24% of them
- Reviews138#104 of 151 by number of reviews
- Length5.4 hoursshorter than 52% of them
- Last updatedFebruary 2026more recently than 82% of them
Course Description
Are you ready to learn Apache Spark the practical way — by analyzing 120+ years of real Olympic Games data?
In this hands-on project-based course, you’ll use Apache Spark, Spark SQL, and Apache Zeppelin to explore the world’s most exciting sports dataset — the Olympic Games Dataset, containing information on athletes, countries, events, medals, ages, genders, heights, and weights spanning from 1896 to recent editions.
Instead of learning Spark through boring theory, you’ll build a complete analytics project step by step, uncovering insights like:
Which countries dominate Olympic Gold medals?
How have athlete ages and physiques evolved over time?
Are female athletes growing faster in participation than males?
Which sports produce the most champions?
Do athletes over 50 still win medals?
What You Will Learn
By the end of this course, you’ll confidently be able to:
Work with Spark DataFrames and Spark SQL
Load real datasets using Apache Zeppelin Notebooks
Write advanced SQL queries for aggregation, filtering, and joins
Visualize results using Zeppelin bar charts and line charts
Analyze Age, Height, Weight, Gender & Medal trends across decades
Build a portfolio-ready Olympic Analytics Dashboard
Tools You’ll Use
Tools Purpose
Apache Spark Big Data Processing
Spark SQL Querying and Analysis
Apache Zeppelin Interactive Notebooks & Visualization
Docker / Java Environment Setup
Don’t worry if you’ve never installed Spark before — we guide you through Java installation, Docker setup, Zeppelin configuration, and Spark Interpreter connection — all step by step.
Who is This Course For?
This course is beginner-friendly and perfect for:
Aspiring Data Engineers / Analysts
Students learning Spark & SQL through real projects
Anyone who prefers hands-on learning over theory
No prior Spark experience is required — just basic familiarity with SQL or Python is enough to get started.
Final Output — A Real Big Data Analytics Project
By the end, you’ll build and present a complete Olympic Analytics Project — something you can proudly showcase on LinkedIn, GitHub, or your Resume.
If you want to master Apache Spark with a fun, engaging, and real-world dataset — this course is for you.
Enroll now and let’s analyze Olympic history with Big Data power!
Instructor Details
- 4.1 Rating
138 Reviews
Bigdata Engineer
I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes
My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.
Responsibilities includes,
- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices
I am sure the knowledge in these courses can give you extra power to win in life.
All the best!!
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Reviews
By Trupti Pawar on 8/5/2024
The topic of course was pretty good for beginners who want ot explore spark. But the explanation was not upto the mark, the instructor is explaining his previous work, and if a person wants to learn it they should be folloing along the instructor , which is not possible.
By Surya on 6/22/2024
Nice one for beginners to understand about SQL and databricks notebook features
By Linda on 12/17/2023
excellent information
By Anonymized User on 9/21/2023
This lecture was very amazing and very helpful for me. Thank you for providing me the knowledge in this field.
By Iqra Abbas on 9/5/2023
It was a great experience.
By Vibha Trehan on 12/1/2022
It's more of a basic course on SQL
By Антон Керн on 9/21/2021
Very, vary and Good!
By D Sai Vamsi Krishna on 1/26/2021
It is very illustrative and nicely explained.
By Praveen Kumar singh on 2/4/2020
This course is good for beginners if you have basic understanding of SQL.There is no much explanation about terms and query.
By Anonymized User on 4/8/2019
Apache Spark with Scala in Databricks is a hot topic and this is one of the first courses that covers this knowledge.
Quality Score
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Overall Score : 82 / 100












