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)
1 learners enrolled in this course.
  • Intermediate friendly
  • 8 hours
  • Self paced
  • 4 lessons
Otavio

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Analyzing Large Datasets in Spark

Enroll For Free

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.

  • 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

Working With RDDs in Spark 2h

Spark DataFrames 2h

Spark SQL 2h

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