Running Ray Jobs with Azure ABS Data

Azure ABS

Azure Data Lake Storage is primarily designed to work with Hadoop and all frameworks that use the Hadoop FileSystem as their data access layer (for example, Spark and Presto). It is a massively scalable, secure data lake functionality built on Azure Blob Storage which is designed for big data analytics and offers a hierarchical file system.

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 Azure ABS for data storage and Ray for compute workloads, it is unsurprising that many organizations are seeking to run Ray jobs with Azure ABS data. Kaspian offers a native connector for this operation. Just register your Azure ABS 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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