Apache Pinot
Distributed OLAP database delivering sub-second queries on fresh streaming data at petabyte scale, ideal for user-facing apps and AI agents requiring real-time insights.
Apache Pinot is an open-source alternative to:
Apache Pinot
Apache Pinot™ is an open-source distributed OLAP database designed for real-time analytics, serving both user-facing applications and AI agents with sub-second query latency on fresh data at petabyte scale.
Key Features
- Sub-Second Queries: Filter and aggregate petabyte datasets with P90 latencies in the tens of milliseconds, enabling interactive live results in UIs.
- High Concurrency: Serve hundreds of thousands of concurrent queries per second for applications and AI agents.
- Real-Time Streaming Ingestion: Ingest data in real-time from Apache Kafka, Apache Pulsar, AWS Kinesis, or batch sources like Hadoop, Spark, and AWS S3.
- Upserts Support: Ingest the same record multiple times while only seeing the latest value at query time (production-tested since version 0.6).
- Versatile Joins: Perform arbitrary fact/dimension and fact/fact joins on petabyte-scale datasets.
- Rich Indexing Options: Pluggable indexes including timestamp, inverted, StarTree, Bloom filter, and range indexes for optimized query performance.
- Multitenant Isolation: Enable customers to explore their own data in real time without pre-aggregation.
- Embedded Analytics: Ship interactive dashboards inside products with support for 100K+ concurrent users querying live data.
Tech / Self-Hosting
Apache Pinot is an open-source project hosted by the Apache Software Foundation. It can be self-hosted on any infrastructure, including cloud, on-premises, or hybrid environments. The project provides comprehensive documentation for deployment, scaling, and management, with support for containerized deployments via Docker and Kubernetes.
Primary Use Cases:
- User-facing analytics (dashboards, metrics APIs, customer-facing reports)
- AI agent-facing analytics (RAG systems, decision engines, agentic observability)
- Real-time decision-making platforms requiring sub-second latency
Supported Data Sources:
- Streaming: Kafka, Pulsar, AWS Kinesis
- Batch: Hadoop, Spark, AWS S3, local files
Deployment Options:
- Self-hosted (bare metal, VMs, containers)
- Cloud-agnostic (AWS, GCP, Azure, etc.)
- Kubernetes-ready with Helm charts available
Performance Characteristics:
- Sub-second query latency on petabyte-scale data
- Horizontal scalability through distributed architecture
- High availability with automatic failover and recovery