In the world of data processing and analytics, Spark has emerged as a powerful tool that empowers developers and data engineers to handle structured and semi-structured data efficiently. By leveraging the distributed processing capabilities of Apache Spark, Spark can effortlessly manage large datasets and execute transformations in parallel across multiple partitions.
When working with large volumes of data, Spark partitions the data and distributes it across the cluster of machines. Spark will perform transformations on each partition independently, leading to improved performance and scalability. In this article, we will explore how to read data from different tables, perform a join operation, and transform the result into a JSON structure using Java Spark SQL code. JSON is a widely used data format for exchanging between systems.