Data-centric businesses increasingly rely on real-time data analytics to make quick, targeted decisions that enhance customer satisfaction and predict future demands. The process involves capturing and processing streaming data in real-time, often requiring the integration of multiple systems for data ingestion, processing, and storage. This article illustrates how to build a real-time decision-making system using Redpanda, a Kafka-compatible real-time data platform, and Google’s BigQuery, a serverless data warehouse. The guide details setting up a system to display hotel room availability in real-time, using Redpanda for data ingestion and BigQuery for storage and processing, facilitated by Kafka Connect. It highlights BigQuery’s comprehensive SQL support and rapid execution as advantageous for low-latency analytics. The tutorial demonstrates the creation of a local Redpanda cluster, setting up BigQuery tables, configuring Kafka Connect, and running SQL queries to analyze booking events, ultimately providing insights into room availability. Additionally, the tutorial suggests that Redpanda can integrate with other data platforms like Snowflake and Google Cloud Storage, offering flexibility depending on the application’s specific needs.