About us
The Real-Time Analytics meetup covers a range of topics around building Real Time Analytics systems; including use cases, technical deep dives, and best practices.
Interested in speaking, organizing, or volunteering? Contact community@startree.ai
This meetup is organized by the founders of StarTree and original creators of Apache Pinot:
Apache Pinot is a realtime distributed OLAP datastore, used to deliver scalable real time analytics with low latency. It can ingest data from batch data sources (S3, HDFS, Azure Data Lake, Google Cloud Storage) as well as streaming sources (such as Kafka). Pinot is used extensively at LinkedIn and Uber to power many analytical applications such as Who Viewed My Profile, Ad Analytics, Talent Analytics, Uber Eats and many more serving 200k+ queries per second while ingesting 1Million+ events per second.
Resources
> • What is Apache Pinot? https://www.startree.ai/what-is-apache-pinot
> • Launching At LinkedIn: The Story of Apache Pinot: https://www.startree.ai/blog/launching-at-linkedin-the-story-of-apache-pinot
> • For more info on Apache Pinot go to dev.startree.ai
> •Our community is active on slack! To join our slack, go to stree.ai/slack
Upcoming events
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- Network event
Apache Pinot Meetup: 2026 Year in Review
·OnlineOnline1 attendee from 4 groupsHow did Apache Pinot evolve in 2026 to support growing datasets, more demanding queries, and new application workloads—and where is it headed next?
Join the Apache Pinot community on December 10 to find out as we explore The New Scale of Demand: Applications, Agents, and Data Growth.
This year-in-review meetup brings contributors and practitioners together for an open, show-and-tell discussion of the architecture decisions, production experiences, and community contributions behind scaling Pinot.
What to expect:
- Production lessons: How teams approach capacity planning, tenant isolation, and demanding queries while protecting latency and reliability.
- Evolving architectures: Where Apache Iceberg and Delta Lake fit as teams connect lakehouse data with application needs.
- Emerging workloads: What AI agents could require from analytics infrastructure, and which scaling challenges deserve attention next.
Bring your questions, compare approaches with peers, and leave with ideas to apply to your own platform.
Past events
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