Imagine you are building an analytics platform for an e-commerce company. Every day, you need to process 50 million user events, join them with product catalogs and user profiles, compute aggregated metrics, train recommendation models, and populate dashboards.

The data sits across S3, your data warehouse, and streaming from Kafka. A single machine would take days. You need distributed processing, but you also need a unified platform that handles all these workloads without stitching together five different tools.

This is exactly the problem Apache Spark was designed to solve.

Spark is not just a faster MapReduce replacement. It is a unified analytics engine that handles batch processing, streaming, machine learning, and graph processing through a single API. Spark sits at the intersection of many common requirements: large-scale ETL, data lake processing, feature engineering for ML, and real-time analytics.

This guide covers the practical knowledge you need to discuss Spark confidently in interviews. We will explore the core abstractions, execution model, query optimization, streaming capabilities, and performance tuning strategies that come up most often.

1. When to Choose Spark