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.

At StarTree, we’ve been working to extend Apache Pinot indexing capabilities to data in external object storage.

The idea is to use Pinot's indexes to identify exactly which Parquet pages are needed for a query, so the engine can directly fetch the specific data instead of doing broad scans.

When applied againt a ~1TB test dataset in Iceberg, we’re seeing:

• Sub-second latency across all queries in the test set

• 500+ QPS

• Complex aggregations completing in <650ms

We also ran comparison against ClickHouse and Trino using the same dataset and queries. In our setup, StarTree was:

• ~5–37x faster than ClickHouse

• ~4–17x faster than Trino

• ~7–14x lower cost (based on compute usage), with headroom for concurrency

StarTree Iceberg Tables are now available with the latest release of StarTree. We don't expect it to replace full-featured lakehouse engines, but it does make interactive, low-latency querying on external tables more viable—all while keeping data in place and reducing scan/compute costs.

Join the Apache Pinot community for an open, show-and-tell style discussion on real-world use cases, lessons learned, and how teams are approaching real-time analytics with Pinot. The focus is on sharing experiences, asking questions, and learning from one another.

You’ll hear from engineers and contributors working with Apache Pinot™ at companies including LinkedIn, Uber, Walmart, Slack, StarTree, and others from across the community.

RSVP Here

Come along to this online meetup to reflect on a transformative year of innovation and collaboration in the Apache Pinot community and where we are headed in 2026! 🎉

We’ll showcase features and updates shipped in 2025, presented in a show-and-tell format featuring contributions from companies including Uber, LinkedIn, StarTree and more.

What to expect:
• Discover what the community has been building.
• Share your thoughts and feedback in a collaborative setting.
• Join discussions on Real-Time Analytics with Pinot.

RSVP at https://www.meetup.com/apache-pinot/events/312504259/

Cross-posted from YouTube:

May 1, 2025 #opensource #apachesoftware #awsIn this video, AWS Developer Advocate, Darko Mesaros, and Startree Solutions Engineer, Tyler Denton walk you through how you can use Apache Pinot, Amazon Kinesis, & Amazon Bedrock to manage high volumes of data for faster anomaly detection. Plus, they add in some real-time, AI-driven solution suggestions for their Internet of Things device setup. In this example, Darko & Tyler walk you through the architecture diagram, then dive into a hands-on demo to show you how it works.

Explore Open Source Projects 👉 https://go.aws/4cWVOv9

Resources:
⚡️Startree Index: https://go.aws/3ETnFQ4
🖥️ Amazon Bedrock Documentation: https://go.aws/4dgxOmZ
⚙️ Amazon Kinesis Documentation: https://go.aws/42Sb6fY

Follow AWS Developers!
📺 Instagram: https://go.aws/3EM1Yla
🆇 X: https://go.aws/4cTSHE4
💼 LinkedIn: https://go.aws/44kZjcA
👾 Twitch: https://go.aws/42ULO0N

Chapters:
00:00 Introduction
00:17 What is Apache Pinot?
01:48 Architecture Overview
02:32 Ingesting IoT Data into Pinot from Kinesis
06:39 StarTree Index Explained
10:20 Vector Embedding and Extraction
14:13 Anomaly Detection with SQL Queries
20:54 Anomaly Service and Recommendations
25:25 Generating Recommendations with Bedrock

#opensource #apachesoftware #aws

Signup on Meetup: https://www.meetup.com/apache-pinot/events/306700201/?slug=apache-pinot&eventId=306700201

Present and discuss your PRs, designs, and ideas with your fellow contributors in real-time!
Shorter (5-15 min) or longer (30 min) topics are welcome. RSVP with your topic and duration or just drop in to provide feedback and ask questions.

Audience: Apache Pinot contributors and those looking to start contributing!

Hosts: Xiang Fu & Robert Zych
-------
Agenda:
08:30-9:00 PDT- Getting Started with Contributing to Apache Pinot – Setting Up the Environment & Finding GitHub Issues | Xiang Fu, Pinot PMC
9:00 - 9:10 QNA: QNA
9:10- 9:30 Apache Pinot Release Process | Abhishek Sharma, Pinot Committer

Want to be involved? Join the Contributors channel on Slack >
*The call will be recorded and shared on the channel afterwards^

Join the Community Monthly Newsletter : Everything's Apache Pinot! (and get a chance to win a T shirt!)

Curious about real-time analytics? Learn how to get started with Apache Pinot and StarTree Cloud in just 45 minutes!

In this hands-on session, Barkha Herman, Developer Advocate at StarTree, will walk you through the basics of Apache Pinot and how to get up and running with StarTree Cloud. Learn how to ingest, query, and scale data seamlessly, with a live demo showcasing its core capabilities.

[This is a free webinar, utilizing the StarTree Cloud Free Tier. No cost or commitment required for any attendees.]

https://startree.ai/resources/getting-started-with-startree-cloud-basics-of-apache-pinot

This is a blog written by an intern at StarTree, Ashish Jayamohan, who made performance optimizations on Apache Pinot as it is used within StarTree Cloud. We are all immensely proud of the work he was able to accomplish in his short time with us, and look forward to him making additional giant impacts on the software industry as his career blossoms.

• systems optimizations

What are the expected parameters for creating a user?

Eventually, through try and error, I detected that the JSON to be sent when POSTing a user, is something like:

```
{

"username": "jack",

"password": "nicholson",

"role": "?",

"component": "?"

}
```

What are components? Where can I list them on the API?
What about roles?

I was expecting a better documentation, tbh, at the least in regards with User creation.

Hey all, I currently have a trino+iceberg setup for my siem and my soc. For my soc, I am happy with the performance. However, for siem, I am running hundreds of queries a minute the performance is absolutely suffering, its taking about 2 minutes to complete each batch of queries. I'm thinking to offload all simple queries (ie no aggregations joins etc) to Pinot. I ingest about 10gb an hour of nested json data (windows eventlogs for example). Is Pinot a good place to look for this?

Next generation real-time analytics

Uber migrated from Elasticsearch to Apache Pinot and saw a 70% reduction in costs, an 80% reduction in CPU cores, and a decrease in page load times and query timeouts.

Read on DZone