Migrating Apache Spark workloads from CPUs to GPUs on Amazon EMR can dramatically improve performance, reduce cloud costs, and accelerate data processing. Traditional CPU-based Spark pipelines are often slow, resource-intensive, and expensive to scale. GPU-accelerated Spark, powered by the RAPIDS Accelerator, leverages parallel processing to deliver significant speedups. To simplify this transition, NVIDIA has introduced Project Aether—a tool designed to automate the migration of existing CPU-based Spark jobs on Amazon EMR to GPU-accelerated environments. Project Aether is a suite of microservices that streamline the entire migration process, reducing manual effort and minimizing risk. It integrates directly with Amazon EMR, enabling automated management of GPU test clusters and the conversion and optimization of Spark jobs. The tool is especially useful for organizations looking to modernize their data pipelines without rewriting code or retraining teams. To get started, users must install the Aether package and configure the client for the EMR platform using simple commands. The migration workflow is structured into four key phases: predict, optimize, validate, and migrate. In the predict phase, the qualification tool assesses whether a CPU Spark job is suitable for GPU acceleration. It uses a machine learning model called QualX, based on XGBoost, to analyze the CPU event log and predict potential speedup and compatibility. This step provides early insights into the job’s migration potential. The optimize phase automates performance tuning. It creates a GPU test cluster using the cluster service, then submits the job with initial configurations. The profile service analyzes GPU event logs to identify bottlenecks and suggests improved Spark settings. This process is repeated iteratively—submit, profile, adjust—until optimal performance and cost efficiency are achieved. The validate phase ensures data integrity. It compares key metrics such as rows read and rows written between the original CPU job and the best-performing GPU run. This step confirms that the GPU version produces identical results, maintaining data accuracy. The migrate phase generates detailed reports on the migration process. The report service provides both CLI and UI access to view job history, performance improvements, recommended Spark configurations, and optimal GPU cluster settings. These insights help teams make informed decisions and scale the migration across their workload portfolio. All these steps can be combined into a single automated run using a unified Aether command, significantly reducing the time and complexity of large-scale migrations. Project Aether is a game-changer for organizations aiming to unlock the full potential of GPU-accelerated data processing. It enables faster, more efficient Spark workloads with lower cloud costs and reduced development overhead. For teams ready to modernize their data infrastructure, access to Project Aether is available by application. For more information on the RAPIDS Accelerator for Apache Spark, refer to the official documentation.