Running R Jobs with Snowflake Data

Snowflake

Snowflake is a cloud-based data warehousing company that provides a platform for data storage, processing and analysis. It is considered cloud-agnostic, as it operates across Amazon Web Services (AWS), Microsoft Azure or Google Cloud. Snowflake delivers a platform that is fast, flexible, and user-friendly. It provides the means for not only data storage but also processing and analysis. One of the main reasons that Snowflake is gaining recognition as the top cloud data warehousing solution is thanks to its architecture, which consists of dynamic, scalable computing power with usage-based charges out-of-the-box features such as data cloning and sharing, on-the-fly scalable computing and third-party tool support.

R

R is a programming language that was created for statistical computing and graphics. It has become increasingly popular over the years because of its flexibility, ease of use, and powerful data analysis capabilities. R has a rich ecosystem with complex data models and elegant tools for data reporting. It offers a wide variety of statistics-related libraries and provides a favorable environment for statistical computing and design. R is especially popular among data scientists, statisticians, and researchers who work with large datasets. It is used for data analysis, statistical inference, machine learning algorithms, and data visualization. R can also be used for creating reproducible, high-quality research reports.

With the growing popularity of both Snowflake for data storage and R for compute workloads, it is unsurprising that many organizations are seeking to run R jobs with Snowflake data. Kaspian offers a native connector for this operation. Just register your Snowflake datastore and link your R 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.