Use Cases Powered by Apache Pinot
From real-time dashboards to personalized recommendations, Pinot powers modern applications that need fast, scalable analytics on petabyte-scale data.
Real-Time Dashboards
Power internal and external dashboards with sub-second query latency on fresh data. Serve dynamic, interactive visualizations to teams and customers.
- •Sub-second query latency for interactive dashboard updates
- •Fresh data ingested in real-time from streaming sources
- •Support for complex aggregations and filters
In Production at Stripe
Stripe uses Pinot to power real-time billing dashboards, serving 10K+ queries/sec with sub-second latency while tracking $18.6B in transactions during Black Friday-Cyber Monday.
User-Facing Analytics
Embed analytics directly in your product, serving hundreds of thousands of concurrent queries. Enable your users to explore data in real-time.
- •Handle hundreds of thousands of concurrent queries per second
- •Horizontally scalable architecture for growing demand
- •Built-in multitenancy for product isolation
In Production at LinkedIn
LinkedIn built Pinot to power "Who Viewed Your Profile" and 50+ other user-facing apps, serving 250K+ queries/sec across 700M+ members.
Anomaly Detection
Detect anomalies in real-time across metrics from streaming sources like Kafka. Build proactive alerting systems and catch issues as they happen.
- •Ingest data in real-time from Kafka, Pulsar, and Kinesis
- •Query freshly ingested data immediately for anomaly detection
- •High-concurrency support for continuous monitoring systems
In Production at Cisco Webex
Webex processes 100+ TB of telemetry data daily through Pinot for real-time observability and anomaly detection, replacing Elasticsearch and eliminating 500+ nodes.
Ad-Hoc OLAP Queries
Run flexible, exploratory analytical queries on petabyte-scale datasets. Support data exploration and business intelligence workloads.
- •Query petabyte-scale datasets with millisecond latencies
- •SQL interface for flexible analytical queries
- •Rich indexing options for optimized query performance
In Production at Uber
Uber runs 100+ offline analytics use cases with 500+ production tables in Pinot, serving sub-second queries for inventory, catalog, and business intelligence workloads.
Event Analytics
Analyze clickstreams, app events, and IoT data in real time. Understand user behavior and system performance with immediate insights.
- •Batch and streaming ingestion from diverse sources
- •Efficient storage and querying of high-volume events
- •Support for time-series analysis and windowing
In Production at DoorDash
DoorDash tracks ad impressions, clicks, and orders across 500+ dimensions in real time using Pinot, reducing query latency from 30-second timeouts to under 100ms.
Personalization & Recommendations
Power real-time recommendation engines with millisecond lookup times. Deliver personalized experiences based on fresh user behavior data.
- •Millisecond-latency lookups for real-time personalization
- •Support for complex joins between user and behavioral data
- •Upserts for real-time profile updates
In Production at LinkedIn
LinkedIn uses Pinot to compute near-real-time features for feed personalization, retrieving member actions with attributes in under 50ms at 20,000+ queries/sec.
