Olympic Games Analytics Project in Apache Spark for beginner

Explore the three data categories: performance, demographics, and events, and see how they enable multi-dimensional analytics for Olympic data using Apache Spark.

Explore the Olympic dataset structure in Spark, detailing attributes like id, name, sex, age, height, weight, team, NOC, game, city, sports, events, medals, metadata, and linked files.

Install OpenJDK 11 to prepare for Apache Zeppelin. Update packages and verify the Java version after installation.

Set up a persistent Java environment by editing /etc/profile to export Java home, JRE, and PATH, then verify the setup with echo $JAVA_HOME to confirm the Java 11 installation.

Install Docker Desktop on Windows 10/11, download and run as administrator, launch Docker, watch it load, and explore containers, images, volumes, and build features in Docker Desktop.

Learn to run Apache Zeppelin on Docker Desktop for Windows by pulling the latest Zeppelin image, launching a container on port 8080, and managing start-stop and logs for notebook access.

Configure and connect Apache Spark with Zeppelin by installing Spark, launching the Spark shell locally, and setting Zeppelin interpreter options for Spark master local and app name.

Import the Olympic Games Analytics Zeppelin file into the Apache Zeppelin environment from the downloads folder, then open it to access the ready-to-run code for this beginner Apache Spark project.

Learn to document, annotate, and structure Apache Zeppelin notebooks with markdown, creating readable, presentation ready reports using headers, lists, links, images, and tables.

Learn to create, edit, and run paragraphs in Apache Zeppelin, using Spark and SQL interpreters, markdown documentation, and visualizations to build modular data pipelines from CSV to insights.

Explore how to create tables, bar charts, line charts, pie charts, and scatter plots in Apache Zeppelin to turn raw data into clear visual insights and interactive dashboards.

Configure and use the Apache Zeppelin Spark interpreter to write, run, and visualize Spark jobs in a notebook, leveraging Spark SQL, data frames, and RDDs.

Master Spark SQL queries on data frames in Apache Zeppelin, register data frames as temp views, and visualize results with built-in charts while leveraging caching for faster interactive analytics.

Visualize spark outputs in Apache Zeppelin by turning Spark data frames into interactive tables and charts, using temporary views and Spark SQL for flexible visual exploration.

Track spark job performance and tune execution with Apache Zeppelin. Monitor via the spark web UI, identify bottlenecks like skew and shuffles, and apply caching, repartitioning, and broadcasting joins.

Create a temporary SQL view from the athlete final data frame to enable easy SQL queries and quick aggregation, bridging data frame and SQL analysis in the Olympic Games project.

Load the noc region data set from a csv, infer its schema, and register a temporary sql view for spark sql queries like select region, count, and group by region.

Analyze the age distribution of Olympic gold medalists with spark sql, grouping by age to count medals and visualize results with a bar chart.

Analyze gold medal wins by athletes over 50 using Apache Spark for beginners, filter gold medalists aged 50+, and extract sports and ages for visualization.

Analyze how women athletes earned medals in the Summer Olympics, counting bronze, silver, and gold by year with Apache Spark and SQL, and show the growing participation over time.

Explore how Apache Spark analyzes Olympic gold medals to identify the top five countries, using a Spark SQL join and aggregation to reveal USA, Russia, Germany, UK, and Italy.

Track weight trends of male and female gymnasts over time using Spark SQL queries to compute minimum, mean, and maximum weights by year after 1950, and visualize with line charts.

Analyze how male and female Olympic weightlifters' body weight evolves over time. Spark sql computes min, mean, and max weights by year from 1950 onward for both genders.

Analyze how the heights of male and female Olympic weightlifters evolved over time using spark sql to compute minimum, mean, and maximum heights, revealing biomechanics and sports science trends.

Analyze silver medal distribution by country in Olympic games using Spark SQL joins. Identify leaders and trends: USA, Great Britain, Russia, and Germany lead.

Analyze bronze medal distribution by country using Spark SQL joins and group by, counting medals per NOC and visualizing with a bar chart in Apache Zeppelin.

Analyze Olympic Games data with Apache Spark in a beginner-friendly project, using Databricks Community Edition to load data, build a notebook, and visualize medal distributions by country, gender, and discipline.

Identify the file level details for the Olympic Games analytics project and determine which files you need. Download Windows, Linux, or Mac to support your tools.

Visit Databricks at databricks.com, click get started for free, and choose community edition to receive credentials by email. Log in with credentials to access the community edition for free use.

Import a Databricks notebook by uploading the required files through the import tab, completing the import process, and reviewing the e commerce report setup.

Explore the Olympic Games analytics project by analyzing a historical dataset with Apache Spark and SQL, from Athens 1896 to Rio 2016, and learn the project objective and workflow.

Explore the olympic events csv containing 271,116 rows and 15 columns, detailing each athlete's id, name, sex, age, height, weight, theme, noc code, game, season, city, sport, event, and medal.

Log in to the platform, click the cluster button, create a cluster named sparklers, and watch its status move from pending to active as the supercluster comes up.

Load data into a Spark dataframe, infer schema from headers, cast columns to correct types, handle null values, upload files in a notebook, and create temporary views for SQL analysis.

Explore the distribution of ages among Olympic gold medalists by extracting and grouping gold medals by athlete age, then presenting the results with age-based visuals.

Examine gold medals for athletes over 50 across sports using Apache Spark, filter where counts are greater than or equal to 50, and visualize the results.

Explore counting women's medals across summer editions using Apache Spark, aggregating bronze, gold, and silver by edition and visualizing the results.

Explore disciplines with the greatest number of gold medals and identify the USA events that produced those wins, including basketball and swimming.

Analyze year-over-year weight trends for male and female gymnasts, computing the minimum, maximum, and mean weight per year from 1950 onward using Spark SQL.

Explore weight and height trends of Olympic weightlifters over the years using Apache Spark, computing minimum, maximum, and average height and weight for male and female lifters, with visualizations.