Other Business Languages?
Composable Analytics provides a significant advantage to application authors compared to other business process languages like BPEL (Business Process Execution Language), and EMML (Enterprise Mashup Markup Language), which use variables to share information between building blocks. In addition, authors of BPEL workflows or EMML mashups need to specifically use a parallel sequence or parallel ‘foreach’ modules, and structure the workflow to ensure no writes are executed concurrently to shared variables if they want to speed up certain parts of their analytics.
While BPEL is good at long running message exchanges and human interactions, Composable Analytics focuses on bringing together disparate datasets, disseminating results, and producing data feeds/services. Based on our studies, analysts have an easier time comprehending what the analytic is doing and how data is flowing through the application using Composable's flow-based methodology. Developers also enjoy the self-service nature of the platform because they can focus on writing code in the “first class” modules, and they can let the analysts perform the linking and configuration of the application.
While executing the same code across 1000 nodes is exciting, we recognize that analytics these days are more complex. Agile, Just-In-Time Analytics involves multiple stages of processing. Each step might be querying, filtering, aggregating, reducing, scattering, etc., and these steps can be parallelized within the application global view, or within each step. Parallelization is no longer an after thought, but simply built-in from the start.