Dylan vs. Python vs. Querona vs. V Programming Language Comparison Chart

It is dynamic while providing a programming model designed to support efficient machine code generation, including fine-grained control over dynamic and static behaviors. Describes the Open Dylan implementation of the Dylan language, a core set of Dylan libraries, and a library interchange mechanism. The core libraries provide many language extensions, a threads interface, and object finalization, printing and output formatting modules, a streams module, a sockets module, and modules providing an interface to operating system features such as the file system, time and date information, the host machine environment, as well as a foreign function interface and some low-level access to the Microsoft Win32 API.

We make BI & Big Data analytics work easier and faster. Our goal is to empower business users and make always-busy business and heavily loaded BI specialists less dependent on each other when solving data-driven business problems. If you have ever experienced a lack of data you needed, time to consuming report generation or long queue to your BI expert, consider Querona. Querona uses a built-in Big Data engine to handle growing data volumes. Repeatable queries can be cached or calculated in advance. Optimization needs less effort as Querona automatically suggests query improvements. Querona empowers business analysts and data scientists by putting self-service in their hands. They can easily discover and prototype data models, add new data sources, experiment with query optimization and dig in raw data. Less IT is needed. Now users can get live data no matter where it is stored. If databases are too busy to be queried live, Querona will cache the data.