Purpose: The amount of data sources and stored information has been continuously increasing from the moment the first databases were developed. As it often happens, inside a company there are numerous data sources and, unfortunately, one has to combine the information from several sources to get meaningful insights into that data. Efficient information processing and the ability to use it in analytics to derive development strategies is one of the main challenges that businesses have to face nowadays. The main issue, though, is integrating multiple heterogeneous data sources together into a single and coherent source of truth. The issue is further amplified by the massive diversity in data storages, formats, protocols, behaviour and implementation specific nuances of each data source. The integration of event driven and stream-based processes into the data processing pipeline represents a solution that can bring stability, maintainability, extensibility, scalability and consistency across data sources. Findings: Based on the performed research, event driven streaming of data changes is able to provide data processing with minimal delays and make the whole system reactive. This, in turn, increases the velocity of data analysis and reduces the time it takes for the business to respond to real world changes, which may give it a serious advantage over its competitors. Other positive effects include: infrastructure and code maintainability, reduction of data access times and load distribution over multiple instances, effectively reducing the stress in critical points and making the system more fault tolerant. Research limitations/implications: This paper studies the existing data processing strategies, how events and streams can be used to achieve minimal latencies and possible implementation architectures together with advantages and disadvantages of each one of them. Practical implications: The objective is to introduce event driven data change streaming into an existing system and demonstrate the capabilities of such an approach. Originality/value: Data processing has to keep up with the business speed and event driven data change streaming aims to improve the way data is transported and analysed which drives the development and ensures that the business has room for new possibilities Keywords: event, stream, data capture, data change, data processing, extensibility, scalability. Acknowledgment: Present research was evaluated under the guide and with the support of COST CA19136: NET4Age-Friendly the main aim and objective of which is to establish an international and interdisciplinary network of researchers from all sectors to foster awareness,and to support the creation and implementation of smart, healthy indoor and outdoor environments for present and future generations. The results will contribute to solving problems and actions carried out within the COST CA 16226, Indoor living space improvement: Smart Habitat for the Elderly (SHELD-ON), this way creating a better society for everybody.