Running Ray Jobs with AWS S3 Data

AWS S3

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics. Amazon S3 is used as the primary storage platform for a data lake built on AWS because of its virtually unlimited scalability and high durability. AWS-powered data lakes, supported by the unmatched availability of Amazon S3, can handle the scale, agility, and flexibility required to combine different data and analytics approaches.

Ray

Ray is an open-source distributed computing framework that makes it easy to build scalable and efficient applications. It was developed by a team at UC Berkeley's RISELab and has become increasingly popular over the years because of its ability to handle complex workloads with ease. Ray provides a simple API for building distributed applications, making it easy for developers to scale their applications without having to worry about the underlying infrastructure. Ray has been used for a wide variety of applications, including machine learning, reinforcement learning, data processing, and more. It has been adopted by many companies, including Amazon, NVIDIA, and Uber. Ray's popularity can be attributed to its ease of use, scalability, and flexibility.
With the growing popularity of both AWS S3 for data storage and Ray for compute workloads, it is unsurprising that many organizations are seeking to run Ray jobs with AWS S3 data. Kaspian offers a native connector for this operation. Just register your AWS S3 datastore and link your Ray job; Kaspian's autoscaling compute layer makes it easy to crunch through any data in your cloud with minimal setup or management.
Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.

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