CRD Crystal Reports Automation vs. Crystal vs. JavaScript vs. Oracle Machine Learning Comparison Chart

Crystal’s syntax is heavily inspired by Ruby’s, so it feels natural to read and easy to write, and has the added benefit of a lower learning curve for experienced Ruby devs. Crystal is statically type-checked, so any type errors will be caught early by the compiler rather than fail on runtime. Moreover, and to keep the language clean, Crystal has built-in type inference, so most type annotations are unneeded. All types are non-nilable in Crystal, and available variables are represented as a union between the type and nil. As a consequence, the compiler will automatically check for null references in compile time. Crystal’s answer to metaprogramming is a powerful macro system, which ranges from basic templating and AST inspection to types inspection and running arbitrary external programs.

Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.