Apache Pinot

Apache Pinot is LinkedIn's open-source real-time distributed OLAP database, designed for low-latency analytics queries on streaming data — used to power LinkedIn's who-viewed-my-profile and other user-facing analytics features.

What Makes Apache Pinot Stand Out

Originally built at LinkedIn for user-facing analytics at massive scale. Real-time ingestion from Kafka with immediate query availability

Pluggable index types including star-tree index for fast aggregations

Pricing and Plans

Apache Pinot is free and open-source — you can use it without any licensing cost, audit the code, and self-host it for complete data control.

Who Should Use Apache Pinot

Apache Pinot is best for teams and individuals who need analytics databases capabilities and where originally built at linkedin for user-facing analytics at massive scale. It may not be the right fit when more complex than clickhouse for straightforward analytics use cases.

Verdict

Apache Pinot delivers on its core promise as a analytics databases tool. Apache Pinot is LinkedIn's open-source real-time distributed OLAP database, designed for low-latency... For teams evaluating analytics databases options, Apache Pinot is worth considering based on its specific strengths and how they align with your requirements.

Multi-Tenancy and Access Control

Pinot supports multi-tenant deployments where different teams or customers share the same cluster while maintaining data isolation — queries only return data from the requesting tenant's tables without access to other tenants' data.

Pinot in Production

LinkedIn uses Pinot for "Who Viewed Your Profile" — showing users real-time data about profile visits. This production use case established Pinot's reputation for user-facing real-time analytics and informed the architecture decisions that enable sub-second latency at scale.

Upsert and Deduplication

Pinot's upsert capability enables updating existing records — essential for use cases where events represent state changes rather than immutable events. Real-time aggregations reflect the latest state rather than accumulating all historical events.

Schema Management

Pinot's schema definition enables configuring data types, metric aggregations, and dimension columns that optimize query performance for specific access patterns — schema design is the primary lever for optimizing Pinot query performance.

Overall rating: 4.0 / 5

Apache Pinot is the real-time distributed OLAP datastore optimized for user-facing analytics at sub-second latency — used by LinkedIn, Uber, and other companies where millisecond-level analytical query latency is required for real-time dashboards and interactive user features.

Sub-Second Query Latency at Scale

Pinot's architecture achieves sub-second query latency on billions of rows by: storing data in columnar format with pre-computed aggregations, distributing data across many servers that execute queries in parallel, and using multiple index types that optimize common analytical access patterns.

Real-Time Ingestion

Pinot ingests data from Kafka in real time — new events are queryable within seconds of being produced. This low-latency ingestion enables user-facing analytics on fresh data: showing views or interactions from the last few seconds to the user whose activity is being tracked.

Pinot vs. Druid vs. ClickHouse

All three are real-time OLAP databases. Pinot was built specifically for user-facing analytics at the user level (each query returns results for a specific user's context). Druid is stronger for aggregate analytics. ClickHouse has the fastest bulk query performance for analytical workloads that don't require sub-second individual-row access.

Overall rating: 4.0 / 5