Creating and Submitting Java Spark Jobs to Spark Clusters

Dive into an end-to-end data engineering project combining Apache Airflow, Docker, Spark Clusters, Scala, Python, and Java in this comprehensive video tutorial. Learn to create and submit Java Spark jobs to Spark clusters, set up the development environment, and build basic jobs using multiple programming languages. Follow along as the instructor demonstrates how to process data on a Spark cluster and view real-time results. Gain hands-on experience with essential tools and technologies in modern data engineering, including Docker containerization, Airflow workflow management, and Spark distributed computing. By the end of this tutorial, you'll have practical knowledge of creating, compiling, and executing Spark jobs across different programming languages, preparing you for real-world data engineering challenges.

Introduction
Creating The Spark Cluster and Airflow on Docker
Creating Spark Job with Python
Creating Spark Job with Scala
Building and Compiling Scala Jobs
Creating Spark Job with Java
Building and Compiling Java Jobs
Cluster computation results