Real-time data is characterized by its immediate delivery from the source to end users or applications, with minimal processing to ensure low latency and high speed. It is crucial in time-sensitive applications like nautical navigation and patient monitoring, where delays can have significant repercussions. Real-time data comes in two forms: event data, which captures specific incidents, and streaming data, a continuous data flow. Real-time analytics, which provides timely insights from this data, is essential for making rapid decisions in sectors such as finance, security, and IoT. The benefits of real-time data include enhanced decision-making, improved operational efficiency, better customer experiences, and competitive advantages. However, managing real-time data involves challenges like handling high data volume and velocity, minimizing latency, ensuring data quality, and integrating diverse data types while maintaining security and compliance. Technologies like Apache Druid are designed to address these challenges by enabling fast and scalable data processing and analysis, supporting applications across various industries.