Running Ray Jobs with data in your Data Warehouse

Data Warehouse

Data warehouses are popular because they allow organizations to store large amounts of data from disparate sources in one place, making it easier to analyze and make decisions based on that data. Data warehouse software allows you to process, transform, and utilize data for decision-making. Data warehouses can provide a stable, centralized repository for large amounts of historical data. They can improve business processes and decision-making with actionable insights, and can increase a business's data strategy return on investment (ROI) and improve data quality.

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 data warehouses for storage and Ray for compute workloads, it is unsurprising that many organizations are seeking to run Ray jobs with data in their data warehouse. Kaspian offers native connectors for the most popular data warehouses. Just register your data warehouse as a 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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