Running Ray Jobs with GCP GCS Data

GCP GCS

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. Google Cloud Storage (GCS) is a popular object storage service that can be used as a data lake. It provides a simple and cost-effective way to store, manage, and analyze large amounts of data. GCS is designed for very high durability and availability.

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