Data Ingestion Performance Optimization with Spark

Learning Objectives

Understand how inferSchema works internally (two-pass scan mechanism)

Identify performance bottlenecks in data loading operations

Measure and compare execution times with different approaches

Apply column pruning to reduce memory consumption

Overview

This masterclass follows John, a data engineer, as he discovers why his data pipeline is slow and learns to optimize it through schema definition and column pruning. Through self-investigation and hands-on testing, you'll understand how Spark's inferSchema works internally and why defining schemas upfront dramatically improves performance.

Key topics include:

Understanding inferSchema's internal two-pass mechanism
Defining explicit schemas to eliminate redundant file scans
Measuring real performance improvements
Discovering column pruning for memory optimization

Data Ingestion Performance Optimization with Spark

Learning Objectives

Overview

Prerequisites