Testing Apache Spark locally: docker-compose and Kubernetes deployment.

<h1>Introduction</h1> <p>This three-part tutorial series is designed to guide you through different deployment methods for Apache Spark, starting with Docker-compose, progressing to deploying on a Kubernetes cluster using a custom binary Spark image, and finally exploring the convenience of deploying Spark on Kubernetes using Helm charts.</p> <ul> <li><strong>Part 1: Deploy Spark using Docker-compose</strong><br /> In the first part of this tutorial series, we will explore deploying Apache Spark using Docker-compose. Docker-compose simplifies the process of managing multi-container applications and provides an ideal solution for setting up Spark clusters in a development environment. We will cover the creation of a Dockerfile for building the Spark image, configuring the entrypoint script to manage Spark workloads, and creating a Docker Compose file to define the Spark cluster&rsquo;s services. By the end of this part, you will have a working Spark cluster running on your local machine, ready to process data and run Spark jobs.</li> </ul> <p><a href="https://medium.com/@SaphE/testing-apache-spark-locally-docker-compose-and-kubernetes-deployment-94d35a54f222"><strong>Website</strong></a></p>