Course

Analyzing Large Datasets in Spark

As data grows beyond what single machines can handle, Apache Spark has become the industry standard for distributed data processing. In this course, you’ll learn how to leverage Spark’s in-memory computing to process massive datasets 10-100x faster than traditional tools. You’ll master the three core Spark APIs – RDDs for fine-grained control, DataFrames for optimized operations, and Spark SQL for familiar query syntax – giving you the versatility to tackle any big data challenge.

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4.8 (359 reviews)
89 learners enrolled in this course.
  • Intermediate friendly
  • 8 hours
  • Self paced
  • 4 lessons
Otavio

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Otávio Silveira

Data Analyst @ Hortifruti

Analyzing Large Datasets in Spark

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Course overview

Master Apache Spark, the leading framework for big data processing. This hands-on course teaches you to work with Spark’s core data structures – RDDs and DataFrames – while understanding the distributed architecture that makes Spark 10-100x faster than traditional tools. You’ll analyze real datasets including US Census data and Daily Show guests, learning when to use RDDs for custom transformations, DataFrames for optimized operations, and Spark SQL for complex queries. By the end, you’ll confidently process datasets that don’t fit on a single machine.

Key skills

  • Creating and transforming distributed datasets using RDDs, DataFrames, and Spark SQL
  • Building analytical pipelines that process large datasets across distributed clusters
  • Writing optimized Spark applications using lazy evaluation and DataFrame operations
  • Converting between Spark and pandas for comprehensive data analysis workflows

Course outline

Analyzing Large Datasets in Spark [4 lessons]

Introduction to Spark 2h

Lesson Objectives
  • Explain what Apache Spark and PySpark are and why they're used for big data processing
  • Describe Sparks distributed architecture; Driver, Executors, and Cluster components
  • Recognize SparkSession as the modern entry point to Spark functionality
  • Create and interact with RDDs using transformations and actions
  • Set up and verify a complete local PySpark development environment

Working With RDDs in Spark 2h

Lesson Objectives
  • Load and clean real-world CSV data using RDDs
  • Apply core transformations (map, filter) and actions (take, collect, reduce)
  • Understand lazy evaluation and how DAGs optimize processing pipelines
  • Build complete data analysis workflows from raw data to insights

Spark DataFrames 2h

Lesson Objectives
  • Understand DataFrame advantages over RDDs for structured data
  • Create DataFrames from JSON with automatic schema inference
  • Apply filtering, selection, and aggregation operations
  • Chain DataFrame operations into complete analytical pipelines
  • Convert DataFrames to pandas for visualization

Spark SQL 2h

Lesson Objectives
  • Register and query Spark DataFrames using SQL
  • Filter rows and compute new columns using SQL expressions
  • Group and aggregate data using SQL functions
  • Combine multiple views using UNION ALL
  • Compare SQL and DataFrame APIs in Spark

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