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description: This site serves as a collection of documentation about the RAPIDS accelerator for Apache Spark
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# Overview
**If you are a customer looking for information on how to adopt RAPIDS Accelerator for Apache Spark
for your Spark workloads, please go to our User Guide for more information: [link](https://docs.nvidia.com/spark-rapids/user-guide/latest/index.html).**

The RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate processing via the
[RAPIDS libraries](http://rapids.ai).

As data scientists shift from using traditional analytics to leveraging AI(DL/ML) applications that 
better model complex market demands, traditional CPU-based processing can no longer keep up without 
compromising either speed or cost. The growing adoption of AI in analytics has created the need for 
a new framework to process data quickly and cost-efficiently with GPUs.

The RAPIDS Accelerator for Apache Spark combines the power of the [RAPIDS cuDF](https://github.com/rapidsai/cudf/) library and
the scale of the Spark distributed computing framework.  The RAPIDS Accelerator library also has a
built-in accelerated shuffle based on [UCX](https://github.com/openucx/ucx/) that can be configured to leverage GPU-to-GPU
communication and RDMA capabilities.

