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Mastering Big Data with PySpark

Solution: Optimizing PySpark DataFrame Operations

Explore how to enhance PySpark DataFrame operations by analyzing and optimizing code. Learn techniques such as chaining transformations, selective column usage, and built-in aggregations to improve performance and clarity when working with datasets like NYC restaurant orders.

We'll cover the following...

Tasks

Task 1: Review and analyze existing code

  1. Create a SparkSession object and load the orders.csv dataset.
  2. Execute the code snippet to ensure it runs without errors.
  3. Thoroughly review and analyze the provided code snippet, identifying any potential areas for optimization.

Solution for task 1:

Python 3.8

Task 2: Code optimization

In this task, our challenge is to optimize the code to achieve the same results as Task 1 while eliminating unnecessary computations and improving efficiency.

After carefully reviewing and executing the code correctly, modify the code to eliminate unnecessary computations and optimize transformations and actions related to the following tasks:

  • Filtering orders based on specific cuisine types.
  • Aggregating orders by “customer ID” and calculating the total order amount.
  • Applying filters to identify customers with a total order amount exceeding a predefined threshold.
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