Running R Jobs with data in your Data Lake

Data Lake

Data lakes are popular because they provide a consolidated, centralized storage area for raw, unstructured, semi-structured, and structured data taken from multiple sources and lacking a predefined schema. They specialize in ingesting structured, semi-structured and unstructured data and provide mechanisms to easily ingest streaming data in addition to batch loads. Data lakes are open format so users avoid lock-in to a proprietary system like a data warehouse. They are also highly durable and low cost because of their ability to scale and leverage object storage.

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