How to prepare for PySpark Coding Interviews

Ajul Raj

By Ajul Raj

Wed Feb 05 2025

Preparing for PySpark coding interviews can be daunting, but with the right tools and strategies, you can make the process manageable and even enjoyable. In this guide, I’ll share several ways to practice effectively, including setting up PySpark locally, leveraging cloud platforms like Databricks Community Edition, and utilising Spark Playground.

1. Using Databricks Community Edition

Databricks Community Edition is a free, cloud-based platform that provides a collaborative environment for Spark development. It’s a great choice for practicing PySpark without worrying about local setup but it is slow since you have to start a free cluster for running the code.

Databricks Community Edition Login Page

Getting Started

  1. Sign Up: Create an account on the Databricks Community Edition website.
  2. Create a Workspace: Once logged in, set up your workspace and start a cluster. The platform provides a pre-configured Spark environment.
  3. Upload Datasets: Upload sample datasets to the Databricks file system. You can use the “Tables” section or directly upload files in your notebooks.
  4. Write and Execute PySpark Code: Use the interactive notebooks to write PySpark code.

Advantages of Databricks

2. Spark Playground Website

Spark Playground is a dedicated platform for practicing PySpark online. It’s perfect for quick hands-on practice without any setup. No cluster setup required to run the code!

PySpark Online Compiler on Spark Playground

Features of Spark Playground

3. Setting Up PySpark Locally

Practicing PySpark locally is an excellent way to get familiar with its APIs and configurations. Here’s how you can set up a local environment:

Step-by-Step Setup

  1. Install Java: PySpark requires Java to run. Download and install the latest version of Java Development Kit (JDK) from the Oracle website or OpenJDK.
  2. Install Spark: Download Apache Spark from the official Spark website. Choose the version that matches your Hadoop setup (standalone mode works for most practice scenarios).
  3. Set Environment Variables: Configure your system’s environment variables to include Spark and Java paths. For example:
  4. Install PySpark: Use pip to install PySpark in your Python environment:
1export SPARK_HOME=/path/to/sparkexport PATH=$SPARK_HOME/bin:$PATHexport JAVA_HOME=/path/to/java
1pip install pyspark

Local Practice Tips

Final Tips for Interview Preparation

I prefer using Spark Playground for quickly running the PySpark code and practice some questions. I also prefer Databricks community version for hands on practice creating the pipeline.