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Apache Pinot

Data
Open source · Apache-2.0

Apache Pinot is a realtime distributed OLAP datastore built for low-latency analytical queries at scale — think sub-second aggregations over billions of rows, serving both realtime streaming data (Kafka, Kinesis) and batch data (Hive, S3, H

updated 2026-07-01

What it is

Apache Pinot is a realtime distributed OLAP datastore built for low-latency analytical queries at scale — think sub-second aggregations over billions of rows, serving both realtime streaming data (Kafka, Kinesis) and batch data (Hive, S3, HDFS) from the same table. It's the engine behind user-facing analytics dashboards, not back-office BI. Pinot uses a segment-based columnar storage model with pluggable indexes (inverted, sorted, star-tree, bloom, text) and a Presto/Trino/Calcite-style SQL interface. Architecturally it splits into controllers, brokers, servers, and minions — each independently scalable, which is where most of the operational complexity lives.

Who builds it and why

Originated at LinkedIn to power internal analytics products (think "who viewed your profile," site analytics at scale), later donated to Apache. Now an Apache Software Foundation project with contributions from LinkedIn, Uber, StarTree (a company built specifically around commercializing Pinot), and others. 476 contributors on GitHub is a real number for a project this technically deep — not a corporate-only codebase. StarTree effectively functions as the primary commercial steward, similar to how Confluent relates to Kafka. CNCF maturity level is not publicly available — Pinot is an Apache project, not a CNCF one, so don't expect a CNCF badge here.

Production readiness signal

6,105 GitHub stars and 476 contributors is solid for a specialized OLAP store — this isn't a toy project. Last commit shown as 2026-07-01 with active releases (1.5.1 as latest) indicates ongoing development velocity. Companies running Pinot in production at serious scale include LinkedIn, Uber, Stripe, Walmart, and Slack — these are multi-billion-row, high-QPS deployments, not pilot projects. License is Apache-2.0, so no rug-pull risk on licensing. Founded date isn't publicly documented cleanly, but the project has been battle-tested since roughly 2014-2015 internally at LinkedIn before open-sourcing in 2018.

Who should use this

Teams building user-facing or customer-facing analytics features that need sub-second query latency over high-cardinality, high-volume data — think embedded dashboards, real-time metrics products, ad-tech attribution, fraud detection dashboards, or any product where "analytics" is a feature customers see, not just an internal BI report. If you're ingesting from Kafka and need queries to reflect data within seconds while also querying historical batch data in the same table, Pinot's realtime/offline hybrid table model is purpose-built for this.

Who should NOT use this

Don't use Pinot as your general-purpose data warehouse — it's not a Snowflake/BigQuery replacement for ad-hoc analyst queries, complex joins, or infrequent heavy reporting. Its join support is limited and query patterns work best when denormalized. Don't use it for transactional workloads — it's read-optimized OLAP, not OLTP; no ACID transactions. If your team is small and your query volume/latency requirements don't demand sub-second p99s, you're paying operational complexity tax (controller/broker/server/minion cluster management, ZooKeeper dependency) for nothing — a simpler Postgres or ClickHouse setup will serve you better. Also skip it if you don't have committed streaming infrastructure (Kafka/Kinesis) already — standing that up just to feed Pinot is a lot of new surface area.

Alternatives

  • ClickHouse — similar low-latency OLAP goals, simpler single-binary operational model, less native realtime/streaming-first design.
  • Druid — closest architectural cousin (also LinkedIn/streaming lineage adjacent), strong at similar realtime analytics use cases, generally considered to have a steeper ops learning curve.
  • Rockset (now part of OpenAI, effectively discontinued as a standalone product) — was a fully-managed alternative removing the ops burden; check current status before considering.

Pricing

Fully open source under Apache-2.0 — no licensing cost, no feature gating. StarTree offers a managed cloud version and enterprise support if you want to offload operations, but self-hosting the OSS version is completely free and unrestricted.

Frequently asked

What is Apache Pinot?+
Apache Pinot is a realtime distributed OLAP datastore built for low-latency analytical queries at scale — think sub-second aggregations over billions of rows, serving both realtime streaming data (Kafka, Kinesis) and batch data (Hive, S3, HDFS) from the same table.
Who builds Apache Pinot?+
Originated at LinkedIn to power internal analytics products (think "who viewed your profile," site analytics at scale), later donated to Apache. Now an Apache Software Foundation project with contributions from LinkedIn, Uber, StarTree (a company built specifically around commercializing Pinot), and others.
Is Apache Pinot production ready?+
6,105 GitHub stars and 476 contributors is solid for a specialized OLAP store — this isn't a toy project. Last commit shown as 2026-07-01 with active releases (1.5.1 as latest) indicates ongoing development velocity.
Who should use Apache Pinot?+
Teams building user-facing or customer-facing analytics features that need sub-second query latency over high-cardinality, high-volume data — think embedded dashboards, real-time metrics products, ad-tech attribution, fraud detection dashboards, or any product where "analytics" is a feature customers see, not just an i
Who should not use Apache Pinot?+
Don't use Pinot as your general-purpose data warehouse — it's not a Snowflake/BigQuery replacement for ad-hoc analyst queries, complex joins, or infrequent heavy reporting. Its join support is limited and query patterns work best when denormalized.
What are the alternatives to Apache Pinot?+
ClickHouse — similar low-latency OLAP goals, simpler single-binary operational model, less native realtime/streaming-first design. Druid — closest architectural cousin (also LinkedIn/streaming lineage adjacent), strong at similar realtime analytics use cases, generally considered to have a steeper ops learning curve.
How much does Apache Pinot cost?+
Fully open source under Apache-2.0 — no licensing cost, no feature gating. StarTree offers a managed cloud version and enterprise support if you want to offload operations, but self-hosting the OSS version is completely free and unrestricted.