This exercise focuses on using Spark DataFrames to load structured data from a CSV file and explore its schema. Participants will learn to inspect data types and structure through methods like .printSchema(), .dtypes, and .columns. A key element is defining a Scala case class to create a typed schema, enhancing type safety when converting DataFrames to Datasets. Emphasis is placed on the efficacy of leveraging Scala's static typing alongside Spark's capabilities for better data handling in ETL processes, particularly in business analytics scenarios.
By the end of this exercise, you'll understand how Spark infers or applies schemas when reading data, and how to use Scala case classes to define structured schemas.
The Dataset API allows you to use Scala functions directly, enabling both type safety and readability, especially beneficial when converting DataFrames to a strongly typed Dataset.
Key learning outcomes from this exercise include how to load and explore structured CSV data using Spark DataFrames, and the importance of case classes in Spark Scala projects.
In real-world Spark projects, case classes are crucial in ETL pipelines for handling business entities like customers, transactions, and orders.
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