Introduction
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.
- Part 1: Deploy Spark using Docker-compose
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’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.