apache/pinot

Health v2 · maintenance only

Adoption not part of the score

6128 stars · 1502 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Apache Pinot is an open-source distributed OLAP datastore purpose-built for low-latency, high-throughput real-time analytics. It ingests data from streams like Kafka, Pulsar, and Kinesis (or batch sources) and serves sub-second SQL queries at very high concurrency, supporting both user-facing dashboards and AI agent workloads.

Use cases

• serve sub-second analytics queries for user-facing dashboards
• ingest streaming data from Kafka and query it within seconds
• build embedded analytics APIs with high concurrency
• power real-time leaderboards and metrics endpoints
• provide fresh data retrieval for RAG and LLM agents
• run multi-tenant customer-facing analytics
• feed live signals to fraud detection or bidding engines

When to choose

• you need sub-second (P99 <100ms) queries on fresh streaming data at petabyte scale
• your application serves analytics to many concurrent end users or AI agents
• you need real-time ingestion from Kafka, Pulsar, or Kinesis with immediate queryability
• you want hybrid real-time plus offline tables with upsert/CDC support

When to avoid

• you need complex transactional (OLTP) workloads with joins and updates
• your data is small enough for a single Postgres or MySQL instance
• you only run nightly batch reporting where freshness and latency don't matter
• you lack the operational capacity to run a distributed multi-node cluster

Facets

service · maturity stable

database search-engine analytics streaming etl databases analytics big-data jvm cloud self-hosted olap real-time-analytics distributed-database sql user-facing-analytics columnar-store apache streaming-ingestion vector-search real-time data-engineering docker kubernetes

Member repositories

For agents

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