Week 13/34: Spark Fundamentals for Data Engineers

Understanding Apache Spark and its functionalities for data engineers

Before diving into Data Engineering with Databricks as part of our interview series, we encourage you to familiarise yourself with Apache Spark and common interview questions related to it. Since Databricks is built on top of Spark, some interview questions may focus solely on Spark concepts.

Spark is an open-source distributed computing system that requires manual setup and optimisation, whereas Databricks, built by Spark’s original developers, is a fully managed cloud platform with performance enhancements and easier deployment.

By mastering Spark, you'll be better prepared to navigate Databricks, optimise big data workflows, and take advantage of advanced capabilities with ease.

In this post, we cover:

• Spark architecture with an analogy

• Spark ecosystem

• Scenario-based Spark interview questions with detailed answers.

If you like to:

• Learn the basics of Spark

• Explore key components:

  • Resilient Distributed Datasets (RDDs)

  • DataFrames

  • Spark SQL

• See how to use Spark with a simple application

Check our previous post here :

Getting Started with Apache Spark: Exploring Big Data Processing

For the previous posts of this series, check here: [Data Engineering Interview Preparation Series]¹

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Spark Architecture

Have you ever noticed that restaurants prepare meals much faster than we do at home? If you understand how this works, then you already have a fundamental understanding of how Apache Spark operates.

Let’s elaborate on this statement!

1. In a restaurant, three key roles ensure smooth operations:

   • Master Chef: Oversees the entire process, assigns tasks, and ensures everything runs efficiently.

   • Assistant Chefs (Workers): Handle different parts of meal preparation, working in parallel.

   • Restaurant Manager: Allocates resources, ensuring there are enough workers on busy days and optimising efficiency on slower days.

This mirrors Spark’s architecture!

Just like a restaurant divides tasks to serve meals quickly, Spark processes large-scale data efficiently through parallel computing.

Spark does this by utilising three core components:

1. Driver Program (Master Chef): Initiates the process, understands the computation required, and distributes tasks across worker nodes.