Overview
Apache Pinot is a distributed, column-oriented OLAP datastore designed to provide real-time analytics with millisecond-level latency. Originally developed at LinkedIn to power user-facing analytics such as 'Who viewed my profile,' it has evolved into a cornerstone of the 2026 modern data stack for companies requiring sub-second response times on petabyte-scale datasets. Pinot's architecture is uniquely optimized for high-concurrency workloads, allowing thousands of simultaneous users to query fresh data ingested directly from streaming sources like Apache Kafka, Amazon Kinesis, or Azure Event Hubs. Unlike traditional data warehouses, Pinot utilizes a pluggable indexing strategy—including Star-tree, Bloom filters, and Geospatial indexing—to bypass full table scans. By 2026, Pinot's integration with AI-driven anomaly detection and its support for complex upserts have made it the preferred choice for real-time fraud detection, ad-tech bidding, and live IoT monitoring. It effectively bridges the gap between fast-moving stream processing and deep historical batch analysis, providing a unified SQL interface for hybrid data sources.