Apache Pinot™ is an open-source distributed OLAP database for user-facing and agent-facing real-time analytics. From interactive dashboards to LLM-powered decision engines, Pinot delivers sub-second queries on fresh data at petabyte scale.
Pinot powers both human-facing products and autonomous AI systems with the same real-time engine.
Serve interactive analytics to millions of end users with sub-second query latency at any scale.
Ship interactive dashboards inside your product — 100K+ concurrent users querying live data with sub-second latency.
Let customers explore their own data in real time with multitenant isolation, no pre-aggregation required.
Expose low-latency analytics endpoints that power leaderboards, usage meters, and real-time reporting.
Sub-second SQL over fresh data makes Pinot a natural backend for LLM-powered systems that reason on live state.
Retrieve real-time context from streaming data so LLMs ground answers in facts, not stale snapshots.
Feed live signals — ad bids, fraud scores, pricing — to models that act in milliseconds, not minutes.
Let autonomous agents query system metrics and logs in real time to self-diagnose and self-heal.
Originally developed at LinkedIn, Apache Pinot is an open-source distributed OLAP database for user-facing and agent-facing real-time analytics, delivering sub-second queries on fresh data at very high concurrency.
With its distributed architecture and columnar storage, Apache Pinot empowers businesses to gain valuable insights from real-time data — powering both user-facing and agent-facing applications.
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Filter and aggregate petabyte data sets with P90 latencies in the tens of milliseconds — fast enough to return live results interactively in the UI.
With applications and AI agents querying Pinot directly, it can serve hundreds of thousands of concurrent queries per second.
Ingest from Apache Kafka, Apache Pulsar, and AWS Kinesis in real time. Batch ingest from Hadoop, Spark, AWS S3, and more. Combine batch and streaming sources into a single table for querying.
Ingest the same record many times, but see only the latest value at query time. Upserts are built-in and production-tested since version 0.6.
Choose from pluggable indexes including timestamp, inverted, StarTree, Bloom filter, range, text, JSON, and geospatial options.
Pinot is horizontally scalable and fault-tolerant, adaptable to workloads across the storage and throughput spectrum.
The highly standard SQL query interface is accessible through a built-in query editor and a REST API.
Manage and secure data in isolated logical namespaces for cloud-friendly resource management. Learn more about multitenancy.
Last verified against Apache Pinot 1.4.0 on 2025-09-30
$ docker run -p 9000:9000 apachepinot.docker.scarf.sh/apachepinot/pinot:1.4.0 QuickStart -type hybridcopyThen open localhost:9000 to explore the query console. Full getting started guide →
“Apache Pinot powers over 50 user-facing applications at LinkedIn, serving 250,000+ queries per second with millisecond latency across hundreds of billions of records.”
- LinkedIn Engineering
“Pinot enables us to execute sub-second, petabyte-scale aggregation queries over fresh financial events. During Black Friday-Cyber Monday, Pinot helped us track over $18.6B in transaction volume across 300M+ transactions with P99 latency of 70ms.”
- Peter Bakkum, Stripe
“Uber relies on Apache Pinot for 100+ real-time analytics use cases across the marketplace. Our Neutrino service alone serves 500+ million Pinot queries daily, powering everything from ride tracking to catalog search over 10 billion+ row tables.”
- Uber Engineering
“Apache Pinot replaced Elasticsearch for our real-time observability, delivering 5x to 150x better query performance. We shrank our cluster by 500+ nodes while handling 100+ TB of telemetry data per day with sub-second latency.”
- Cisco Webex Engineering
“We migrated our metrics and alerting platform to Apache Pinot, reducing query latency from 30-second timeouts down to under 100ms. Pinot now powers real-time analytics across 500+ dimensions for our risk and ads platforms.”
- DoorDash Engineering
“Every order on walmart.com flows through Apache Pinot. We ingest 14 million events per minute from Kafka with under 900ms lag, enabling real-time order monitoring and dramatically reducing our Mean Time to Detect and Recover.”
- Walmart Global Tech
“Apache Pinot transformed our payment monitoring from 15-20 minute batch delays to under 1 second data freshness. At peak, we ingest 1 million events per second while tracking 60 billion transactions per year across our platform.”
- Razorpay Engineering
Production-proven at the world's largest internet companies
Based on production deployments at LinkedIn, Stripe, Uber, and other Pinot users. Your results will vary based on hardware, schema design, and query complexity.
Last verified against Apache Pinot 1.4.0 on 2025-09-30
Apache Pinot OSS YouTube Channel is a dedicated video hub for all things Pinot. Our goal is to bring together meetup talks, tutorials, and real-world use cases in one place, making it easier for the community to learn and share.
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