(1) The document discusses using an event streaming platform like Apache Kafka for advanced time series analysis (TSA). Typical processing patterns are described for converting raw data into time series and reconstructing graphs and networks from time series data. (2) A challenge discussed is integrating data streams, experiments, and decision making. The document argues that stream processing using Kafka is better suited than batch processing for real-time business in changing environments and iterative research projects. (3) The document describes approaches for performing time series analysis and network analysis using Kafka to create time series from event streams and graphs from time series pairs. A simplified architecture for complex streaming analytics using reusable building blocks is presented.