n the modern world, streaming data has become widespread in many subject areas. The task of processing streaming data in real time, with minimal delay, is highly relevant.

In stream processing, data processing, various approximate algorithms are often used, which have much higher time and memory efficiency than exact algorithms. In addition, there is often a need to forecast the state of the stream.

Thus, there is currently a need for a tool for sequential snapshotting of aggregated data from streaming data, enabling flow state prediction and approximate algorithms for stream data processing.

The authors of the article have developed such a tool, reviewed its architecture and mechanism of functioning, and evaluated the prospects for its further development.

An analysis of the speed and efficiency of data transfer using the WebSocket and REST API protocols was carried out. To compare the speed of processing stream objects and identify a more reliable technology for developing APIs, expansions of basic functions in Taylor and Fourier series were used. As a result, it was revealed that the REST API is a faster and more accessible resource for transmitting information data in a bitwise transformation, and the scalability of this protocol prevails in the number of processed units, which allows expanding the number of tests performed.

The article describes the main points of the process of creating a system that allows you to manage several cameras simultaneously while saving data on the server. The system has the ability to connect IP cameras and cameras on mobile devices, provide access to other users, and also allows you to watch video online. Hotspots in the architecture of the system have been identified and described. A separate Angular module was developed and design patterns were used. The interaction of the system with the user is described. The stages of further development of cloud video monitoring through ip-cameras with easily accessible control for the end-user are offered.