What's the DE perspective on why R is "bad for production"?

Summary

R is often deemed unsuitable for production environments, especially by data engineers who emphasize efficiency and scalability of processes.

[Why R is not ideal for production]

Data engineers point to R's limitations in production settings, such as sparse support for parallel processing and execution speed. Containerizing applications can mask these issues, but it does not change R's inherently slower nature compared to other languages like Python or Java.

[Impact on the BI market]

This discussion reflects a broader trend within data analysis and BI strategies, where tools and languages not designed for scalability in production are becoming less popular. Competitors such as Python and Scala provide more robustness and flexibility for production applications, compelling BI professionals to reevaluate their strategies.

[What should you do?]

BI professionals should keep an eye on the growing preference for production-friendly languages like Python and consider adapting their skill sets to this trend for future success.