This is a move forward from the imperative way of developing analytics applications. The idea is to make the code more composable, reusable, and easier to write.
However, these libraries do not gel with the imperative nature of the underlying language — the developers need to switch to declarative thinking when using these libraries, and switch back to imperative thinking for other parts of the code. This is not ideal.
Rather than graft declarative constructs in an imperative language, Sclera extends SQL.
SQL has been used for declarative data wrangling and transformation for decades, and is familiar to almost everybody who has ever worked in data management or business intelligence. A recent survey shows that SQL continues to be tremendously popular among developers.
Sclera’s scripting language, ScleraSQL, provides SQL extensions that help express complex analytics tasks in tens of lines instead of hundreds.
ScleraSQL includes extensions for streaming data access, data transformation, data cleaning, machine learning, and pattern matching, as well as “Grammar of Graphics” constructs for declarative visualization, similar to R’s ggplot2.
Data analytics is an extension of business intelligence. It makes sense, therefore, that your analytics language of choice is an extension of SQL.