Apache Druid
High-performance real-time analytics database for sub-second queries on massive datasets.
Real-time distributed OLAP datastore for ultra-low latency analytics at massive scale.
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.
A specialized index that pre-aggregates data across specified dimensions to reduce query complexity from O(n) to O(log n).
High-performance real-time analytics database for sub-second queries on massive datasets.
The industry-standard distributed event streaming platform for high-performance data pipelines and real-time AI telemetry.
Stateful stream processing at scale with sub-millisecond latency and exactly-once consistency.
The first Large Language Model purpose-built for human-to-human conversational intelligence.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Support for updating existing records in real-time segments using a primary key mapping.
Automatically moves older data segments from local SSDs to cheaper object storage like S3 or GCS.
An execution engine that supports distributed joins and complex window functions across nodes.
Built-in H3 and S2 geometry indexes for lightning-fast spatial queries.
Allows for efficient searching and filtering within nested JSON structures without full flattening.
Background processes that merge smaller segments and aggregate old data into larger time buckets.
Traditional warehouses are too slow for millions of users hitting a dashboard simultaneously.
Registry Updated:2/7/2026
Needs to detect anomalous patterns in financial transactions within milliseconds.
Advertisers need to see campaign performance (impressions/clicks) immediately to adjust bids.