Real-time analytics is a powerful approach that enables immediate analysis of big data as it is generated, providing fresh data and fast insights necessary for latency-sensitive applications. Unlike traditional analytics, which relies on historical data and longer processing times, real-time analytics supports rapid decision-making by analyzing streaming data in real-time, making it crucial for applications such as security operations, product analytics, and IoT telemetry. Essential components of a real-time analytics architecture include event streaming, typically facilitated by tools like Apache Kafka and Amazon Kinesis, and a purpose-built database such as Apache Druid, which excels in handling large volumes of streaming data with sub-second query performance. This architecture allows for scalable, low-latency analytics that can handle complex queries and large datasets, as demonstrated by companies like Netflix, which uses real-time analytics for monitoring and ensuring high-quality user experiences. By integrating real-time data with historical insights, organizations can optimize operations, enhance user interactivity, and achieve faster, more informed decision-making, ultimately providing a competitive edge.