Daniel Barsky, a Senior Data Scientist at Augury, shares insights into their use of ScyllaDB for real-time insights in the Industrial IoT sector. While discussing his upcoming presentation at the ScyllaDB Summit 2019, Barsky explains that ScyllaDB is preferred over traditional time-series databases due to its ability to handle high-dimensional time series data and support parallel data processing with tools like Apache Spark. Augury uses ScyllaDB to manage their machine learning models, emphasizing the importance of generating, storing, and retrieving feature data in a streaming setting. Barsky also highlights the company's focus on reducing false positives in IIoT diagnostics through layered detection systems and a "second opinion" process for low-confidence detections. Looking ahead, Augury is transitioning to a streaming pipeline-based architecture using Apache Beam to handle increasing data processing demands, ensuring efficient data enrichment and insight generation. Additionally, Barsky's personal passion for IoT and home automation aligns with his professional work at Augury.