[100% Off] Apache Spark Project World Development Indicators Analytics

World Development Indicators Analytics Project in Apache Spark for beginner using Databricks (Unofficial)

Added on September 26, 2025 Development 2 min read

What you’ll learn

Understand and explore the World Development Indicators dataset from the World Bank.
Set up and configure a free Databricks account and Spark cluster for analytics.
Work with Spark DataFrames to load, transform, and analyze real-world datasets.
Apply Spark SQL and DataFrame operations to generate development insights.
Analyze GINI Index, GDP per capita, literacy rates, life expectancy, poverty rates, infant mortality, and trade statistics across countries.
Compare development trends between rich vs. poor countries over decades.
Perform country-level and global analytics using Spark.
Visualize global development metrics and publish Spark notebooks for sharing insights.
Build confidence in handling real-world Spark projects with practical datasets.

Requirements

No prior Spark experience required — this is a beginner-friendly project.
Basic understanding of Python or SQL will be helpful (but not mandatory).
Access to a computer with internet connection.
A free Databricks account (setup is covered step by step in the course).
Curiosity to learn how data engineering and analytics can provide insights into global development.

Description

Have you ever wondered how countries are compared in terms of GDP, literacy, life expectancy, trade, or poverty rates? Do you want to analyze and visualize global development data with Apache Spark and build powerful insights that tell the story of the world’s economic and social progress?

Welcome to “Apache Spark Project: World Development Indicators Analytics” — a hands-on, project-based course where you will learn to process, analyze, and visualize real-world datasets that capture the world’s development journey

This course focuses on using Apache Spark (with Databricks) to analyze the World Development Indicators dataset from the World Bank. Through step-by-step guidance, you’ll not only learn Spark DataFrame operations and Spark SQL, but also apply them to real analytics use cases that explore critical development metrics

You’ll start from scratch — setting up your environment, understanding the dataset, and importing notebooks into Databricks

Then, we’ll dive deep into hands-on analytics, answering meaningful questions such as:

What is the GINI Index across countries, and what does it reveal about inequality?
How do youth literacy rates compare worldwide?
How do China and India perform in terms of trade as % of GDP?
Which are the richest and poorest countries, and how have they changed between 1960 and 2014?
How do life expectancy, infant mortality, and urban population growth vary across countries?
How has GDP per capita evolved in rich vs poor countries over decades?

By the end of this project, you’ll be able to:

Load and analyze large-scale structured datasets with Spark
Apply Spark DataFrame APIs to generate development insights
Perform country-level comparative analytics.
Publish and share Databricks notebooks as reports.
Build confidence in tackling real-world Spark projects.

This course is designed to give you practical, job-ready skills in Spark through a globally relevant project that connects data engineering, data analysis, and real-world development insights.

Author(s): Bigdata Engineer

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