Use Cases Powered by Apache Pinot
From real-time dashboards to AI-driven decision engines, Pinot powers applications and intelligent systems that need fresh data, sub-second queries, and high concurrency.
User-Facing Analytics
Dashboards, product analytics, embedded analytics, and customer-facing UIs.
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.
Agent-Facing Analytics
AI agents querying live metrics, LLM/RAG retrieval over fresh data, and autonomous decision systems.
LLM Agents Querying Real-Time Data
Give AI agents direct access to fresh, structured data via SQL. LLM-powered systems translate natural language into Pinot queries, enabling non-technical users and autonomous agents to explore real-time analytics without writing code.
- •SQL interface lets LLM agents construct queries programmatically
- •Real-time ingestion keeps the data corpus fresh for AI responses
- •Sub-second latency meets LLM response-time budgets
In Production at MiQ
MiQ built an LLM agent using Google Gemini that translates natural language questions into Pinot SQL, enabling non-technical stakeholders to query real-time programmatic advertising data without writing SQL.
Fraud Detection Decision Engines
Power automated decision systems that evaluate transactions in real time. ML models query fresh signals — payment velocity, unique merchants, and behavioral patterns across time windows — to score and flag fraud within milliseconds.
- •Upserts keep risk profiles current as new signals arrive
- •Time-windowed aggregations compute fraud features in real time
- •High-concurrency queries support burst traffic during peak events
In Production at WePay (JPMorgan Chase)
WePay uses Pinot to compute real-time fraud features — total payments processed and unique credit card merchants across minute/hour/day/week/month windows — consumed by ML fraud detection models.
Automated Observability & Anomaly Detection
Power fully automated monitoring systems that query Pinot on a schedule, run ML-based anomaly detection, and trigger alerts — no human in the loop. From SRE automation to autonomous incident triage, Pinot serves as the real-time analytics layer for intelligent operations.
- •Time-series query engine built on top of Pinot for automated alerting
- •Fresh data from streaming sources for up-to-the-second anomaly detection
- •High QPS supports 100K+ concurrent automated alert evaluations
In Production at Uber
Uber built a time-series query engine on Pinot powering 100,000+ automated alerts in production — fully programmatic anomaly detection with no human in the loop.
Last verified against Apache Pinot 1.4.0 on 2025-09-30
