Real-time data refers to processed data that is made available within milliseconds or seconds of creation, enabling systems to react instantly to events as they occur. This concept is crucial for applications requiring immediate feedback loops, such as fraud detection, anomaly monitoring, personalization engines, and operational dashboards. Real-time processing allows developers to build modern, responsive systems, streamline operations, and power satisfying user experiences by providing immediate insights, improving efficiency, personalized user experiences, and competitive agility. However, it also presents distinct considerations for architects and developers, including higher infrastructure demands, increased architectural complexity, potential for incomplete or inconsistent data, greater development and maintenance effort, scalability bottlenecks, latency-sensitive dependencies, and tooling costs. To build a real-time data pipeline, one must define the use case and data sources, ingest data using a streaming platform, process data in motion, store for fast access or historical reference, serve data to applications or dashboards, and monitor, scale, and optimize the system.