In a rapidly evolving data-centric landscape, effectively managing and analyzing data streams is crucial for businesses to derive valuable insights. Kafka serves as a robust message-broker system that efficiently handles real-time data feeds, while Druid acts as a high-performance real-time analytics database designed for swift queries and low-latency stream ingestion. Automating the ingestion of Kafka topics into Druid can significantly enhance operational efficiency and scalability by reducing manual workloads, eliminating time delays, and ensuring immediate access to the latest data insights. This process involves Python scripts that connect to the Kafka server to detect new topics and automatically initiate the ingestion process into Druid, utilizing features such as schema auto-detection to simplify data processing. The blog provides a comprehensive guide, complete with implementation code, to help organizations streamline their data pipelines and harness the power of their data for improved business performance.