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
1
- Network event
Webinar: Stop Copying Data for Vector Search
·OnlineOnline22 attendees from 10 groupsTo attend, register here.
The data lake is supposed to be where all your data lives. Yet vector search has traditionally required copying embeddings into a separate vector database—adding duplicate storage, synchronization pipelines, and another system to operate. This webinar explores how that architecture is changing.
Tune in for a technical walkthrough of how Apache Pinot brings vector similarity search directly to Apache Iceberg and Delta Lake. We'll cover the evolution from local Pinot tables to tiered storage on Amazon S3 and finally to lake-native vector search using External Tables, showing how approximate nearest neighbor (ANN) search can run over open table formats without moving your data.
In this technical discussion, you'll learn hot to:
- Run vector similarity search directly on Apache Iceberg and Delta Lake using Apache Pinot and External Tables.
- Understand how HNSW enables fast ANN search and what changes are required to make it work over object storage.
- Combine semantic search with SQL filters in a single query over the same data.
- Evaluate a lake-native architecture for AI retrieval that keeps one copy of your data while simplifying search infrastructure.
2 attendees from this group
Past events
9
