Real-time analytics has become essential for modern internet companies to derive insights from raw logs and provide usage analytics to millions of users, exemplified by LinkedIn’s and Uber’s applications. Apache Pinot™, a distributed analytics data store, is emerging as a preferred solution for building scalable real-time analytical applications due to its low-latency performance and ability to handle high-throughput queries. Pinot can ingest data from various sources, including Apache Kafka, and processes this data through a distributed architecture involving Pinot tables, segments, and brokers. It manages real-time data ingestion efficiently by creating mutable and immutable segments, utilizing off-heap memory management to improve resource efficiency and cluster stability. Pinot's integration with Kafka allows for minimal coordination between replicas, and the system is designed to handle complex queries while ensuring data freshness and low query latency. Through advanced memory management techniques, Pinot reduces memory pressure and enhances performance, making it a robust choice for real-time data processing.