Running R 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.

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 data warehouses for storage and R for compute workloads, it is unsurprising that many organizations are seeking to run R 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 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.