Sigmoid provides visual analysis and machine learning platforms specifically targeted at big data and real-time data streams. The SigView visual analytics platform is typical of contemporary technologies with dashboards, graphs, charts, tables and the ability to drill down as required. It doesn’t offer the finesse of a product like Sisense or Tableau, but the Spark architecture ensures very high levels of performance, and the platform is very good at handling live streaming data. SamVeda on the other hand is a real-time machine learning platform. It focuses on pattern recognition within high volumes of streaming data, employing unsupervised learning methods. Typical applications include IoT device and sensor data pattern recognition and anomaly detection – patterns of behavior that are atypical and require investigation. As with SigView, it is based on Apache Spark and its own NitroDb database. Private and hybrid cloud, and on-premise deployments are supported. Sigmoid also provide an analytics database in the form of NitroDb. This is capable of handling hundreds of terabytes of data and delivering sub 5 second responses.
A variety of solutions are offered, demonstrating the most common areas of applicability for the technology. These include advertising analytics, transport and logistic analytics, retail analytics and sensor analytics. The emphasis is primarily on speed, hence the use of Spark technologies and its own NitroDb technology.
The visual analytics environment provided by SigView will be familiar to anyone who has used products such as Qlik Sense and Tableau. It provides all the usual facilities, including a rich set of visualizations, pre-built connectors, API access to raw data, the ability to create custom metrics, account management, admin tools, and an inbuilt in-memory columnar database (NitroDb) that scales extremely well. Sigmoid offers fully managed services and boasts some of the lowest TCO figures when compared with similar platforms.
The product distinguishes itself by its ability to handle very high volumes of data and still provide very rapid responses – typically sub-second for all but the largest databases.
The analysis of real-time, high volume, streaming data is the speciality of SamVeda, and it applies sophisticated machine learning technologies to identify patterns in data streams, and particularly the identification of aberrant behavior. It is particularly well suited to the analysis of data streaming from IoT sensors and devices, providing graphical analysis of data streams and the highlighting of anomalies.
The technology is capable of analyzing streams from literally thousands of inputs concurrently. A dictionary of patterns is created, allowing actions to be triggered when particular patterns occur. Obviously this requires feedback from the business, so that relevant actions can be defined.