EVA is an interactive visualization environment developed by ISGNEURO. It is unique because it combines imperative and declarative approaches to organizing user interface description and allows users to implement the complex interactive dashboards logic without any programming.
EVA is designed for preliminary interactive data research and it is also the simplest tool for creating high-level final dashboards.

EVA consists of:

• A data analysis tool with the option to export analysis results as ready-to-use dashboards.
• Dashboard management environment.

Apache Zeppelin is a popular Apache open source solution designed to implement a single interface for visualizing data obtained as a result of its analysis by various tools.

OTL is a high-level data processing language. It was originally designed on the basis of Splunk's SPL language and is backward compatible with SPL for most of its features.
The OTL language is a highly efficient solution to a number of processing tasks, especially in machine-generated logs.

Main functions of the language are:

• full-text search and filtering on all message content;
• selection and search by selected fields;
• aggregation;
• union;
• data enrichment from external or internal sources;
• calculation of statistical indicators;
• mathematical data transformations;
• data preparation for specific types of visualization;
• train and apply machine learning models from a wide range of open source libraries.

A fundamental feature of OTL is high efficiency of using the parallelizing computations possibilities over a computing resources cluster both in near real-time and batch processing modes.

SMaLL (Simple Machine Learning Language) is language that extends the capabilities of OTL and allows users to use all the features of the WDC-Platform without machine learning techniques or data engineering knowledge.
The main idea of SMaLL is to reduce the solution of most analytical problems to three standard fixed command pipeline sequences - patterns:

• Model training pattern (GetData | Fit | Explain | Score | Show).
• Model application pattern (GetData | Apply | Score | Show).
• Directories pattern (GetData | Eval | Put).

These sequences are constant and differ only by specifying different profiles as operands - selected areas for which plugin sets are created - specialized data preprocessors and machine learning algorithms specific to a particular area of knowledge.
Such approach allows to hide the processing data routine details from the end user and concentrate his/her efforts on achieving the final result.
The unique feature of SMaLL is the obligatory Explain command, which interprets the machine learning model and presents it in a human-understandable form. This allows not only to understand and adjust the resulting model, but also bring experts' knowledge into it, which is unavailable in a particular training dataset.