Apache Pinot
Claim this toolUnlock real-time insights from petabyte-scale data with ultra low-latency analytics.
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Reviews on G2
2 reviews tracked The Bottom Line
- Price
- Free, no paid tier. Compare plans
Key facts
- Real-time OLAP datastore for ultra low-latency analytics.
- Scalable and fault-tolerant, handling petabyte data and high concurrency.
- Supports batch and streaming ingestion with rich indexing and SQL interface.
Pros
- Provides ultra low-latency analytics on large datasets.
- Highly scalable and fault-tolerant for demanding workloads.
- Supports both real-time streaming and batch data ingestion.
- Offers a wide range of indexing options for performance optimization.
- Standard SQL interface makes it accessible for data professionals.
Cons
- Requires technical expertise for setup and management.
- As an open-source project, enterprise-grade support might require third-party vendors or community engagement.
What is Apache Pinot?
Apache Pinot is an open-source, distributed OLAP (Online Analytical Processing) datastore designed for lightning-fast insights and real-time analytics. Originally developed at LinkedIn, it provides ultra low-latency queries at extremely high throughput, making it suitable for user-facing analytical applications.
Pinot is built for businesses and developers who need to perform complex aggregations and filtering on large datasets with sub-second response times. Its distributed architecture and columnar storage enable effortless scaling and cost-effective data-driven decisions. It supports both batch and streaming data ingestion from various sources like Kafka, Pulsar, Kinesis, Hadoop, and S3, allowing for a unified view of data.
Key benefits include the ability to serve hundreds of thousands of concurrent queries per second, versatile indexing options for optimized performance, and built-in upsert functionality to handle frequently updated records efficiently. Its standard SQL query interface and multitenancy features further enhance its usability and manageability for diverse analytical workloads.
Available on: Web
Ratings Across the Web
4.8 (2 reviews)
Apache Pinot holds an aggregate rating of 4.8 out of 5 from 2 reviews across G2, last checked October 7, 2026.
Ratings aggregated from independent review platforms. Learn more
Preview
Key Features
- Fast Queries (P90 latencies in tens of milliseconds)
- High Concurrency (hundreds of thousands of concurrent queries per second)
- Batch and Streaming Ingest (from Kafka, Pulsar, Kinesis, Hadoop, Spark, S3)
- Upserts (handle record updates efficiently)
- Versatile Joins (fact/dimension and fact/fact joins)
- Rich Indexing Options (timestamp, inverted, StarTree, Bloom filter, range, text, JSON, geospatial)
- Horizontally Scalable and Fault-Tolerant
- SQL Query Interface (via built-in editor and REST API)
Pricing
Free
Apache Pinot is completely free to use with no hidden costs.
Apache Pinot by the numbers
| Metric | Apache Pinot | Peer median | Position (1 is best) |
|---|---|---|---|
| Free plan | Yes | 6 of 13 have one | n/a |
| Listed as an alternative to | 2 tools | 3 | #8 of 13 (joint) |
Each position is counted among the tools that have data for that row, as noted under it. How we compute this. Advertising and placement fees are not inputs to any position.
Cite this: Toolradar, “Apache Pinot by the numbers”, Oct 2026.
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Apache Pinot FAQ
How does Apache Pinot enable real-time analytics for user-facing applications?
Apache Pinot is designed for ultra low-latency queries on petabyte-scale data, allowing it to serve hundreds of thousands of concurrent queries per second. This capability makes it suitable for powering real-time analytical applications that require immediate insights from large datasets.
What kind of user or team benefits most from Apache Pinot?
Apache Pinot is best suited for businesses and developers who require complex aggregations and filtering on large datasets with sub-second response times. Teams needing to build user-facing analytical applications or perform real-time analytics on big data will find it particularly useful.
How does Apache Pinot compare to ClickHouse for analytical workloads?
Apache Pinot, like ClickHouse, provides ultra low-latency analytics on large datasets. Pinot distinguishes itself with built-in upsert functionality for frequently updated records and its origin as an OLAP datastore developed at LinkedIn for high-throughput, real-time analytics.
What are the main trade-offs when implementing Apache Pinot?
A primary trade-off with Apache Pinot is that it requires technical expertise for setup and ongoing management. Additionally, as an open-source project, enterprise-grade support typically relies on third-party vendors or community engagement rather than direct vendor support.
How is Apache Pinot priced?
Apache Pinot is an open-source project, meaning it is free to use without requiring any paid plans. Users can deploy and operate it without licensing costs.
Can Apache Pinot ingest data from various sources?
Yes, Apache Pinot supports both batch and streaming data ingestion from a variety of sources. It can integrate with platforms like Kafka, Pulsar, Kinesis, Hadoop, and S3 to provide a unified view of data.
Does Apache Pinot offer flexible indexing options?
Apache Pinot provides a wide range of indexing options to optimize performance. These versatile indexing capabilities contribute to its ability to deliver fast query responses on large datasets.
Source: pinot.apache.org