Search Results for "no code"

...Events are just structs with some common fields like :host and :service You can use dozens of built-in streams for filtering, altering, and combining events, or write your own. Since Riemann's configuration is a Clojure program, its syntax is concise, regular, and extendable. Configuration-as-code minimizes boilerplate and gives you the flexibility to adapt to complex situations.

...Built to offer a more performant alternative to dplyr and data.table, it is particularly well-suited for large datasets and econometric applications. It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for grouped operations, weighted statistics, and time series manipulation, making it a compact yet powerful utility for data scientists and researchers working in R.

...For floating-point output either relative or absolute output precision can be specified. UTF-8 encoded strings are supported, including wildcard pattern matching, sets and maps of code points, upper/lowercase, and other Unicode categorizations.

...For instance, there is no need to often pre-aggregate/reduce or generate cubes over your large data sets for ad-hoc visual analytics. This is made possible by smartly managing data in memory, dynamically generating code using vectorization optimizations, and maximizing the potential of modern multi-core CPUs. SnappyData enables complex processing on large data sets in sub-second timeframes.

SPar is an internal C++ Domain-Specific Language (DSL) suitable to model and implement classical stream parallel patterns. The DSL uses standard C++ attributes to introduce annotations tagging the notable components of stream parallel applications: stream sources and stream processing stages. Latest version can be downloaded from the SVN using the following command: svn checkout svn://svn.code.sf.net/p/spar-dsl-compiler/svn/ spar

...Building realtime apps now requires understanding and manually orchestrating multiple systems across the software stack, understanding distributed stream processing, and learning how to deploy and scale realtime systems. The learning curve is quite steep, and most of the initial work involves boilerplate code that is far removed from the primary task of building a realtime app.