Orbit Analytics vs. Rocket Data Virtualization vs. SQL vs. V Programming Language Comparison Chart

Traditional methods of integrating mainframe data, ETL, data warehouses, building connectors, are simply not fast, accurate, or efficient enough for business today. More data than ever before is being created and stored on the mainframe, leaving these old methods further behind. Only data virtualization can close the ever-widening gap to automate the process of making mainframe data broadly accessible to developers and applications. You can curate (discover and map) your data once, then virtualize it for use anywhere, again and again. Finally, your data scales to your business ambitions. Data virtualization on z/OS eliminates the complexity of working with mainframe resources. Using data virtualization, you can knit data from multiple, disconnected sources into a single logical data source, making it much easier to connect mainframe data with your distributed applications. Combine mainframe data with location, social media, and other distributed data.

Simple, fast, safe, and compiled. For developing maintainable software. Simple language for building maintainable programs. You can learn the entire language by going through the documentation over a weekend, and in most cases, there's only one way to do something. This results in simple, readable, and maintainable code. This results in simple, readable, and maintainable code. Despite being simple, V gives a lot of power to the developer and can be used in pretty much every field, including systems programming, webdev, gamedev, GUI, mobile, science, embedded, tooling, etc. V is very similar to Go. If you know Go, you already know 80% of V. Bounds checking, No undefined values, no variable shadowing, immutable variables by default, immutable structs by default, option/result and mandatory error checks, sum types, generics, and immutable function args by default, mutable args have to be marked on call.