Real-time distributed OLAP datastore. Point at Kafka and start querying. Join our growing community at https://xitter.cf/t.co/s0Tg6jKf8W
Mountain View, CA
Joined June 2015
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🚀 #ApachePinot 1.5.0 is out! 🎉
Highlights: enhanced multi‑stage (UNNEST & enriched joins), offline table upsert, federation across clusters, new indexing(N-gram, IFST, combined Lucene), Query Resource Isolation, Perf Optimization, Kafka 4.x support & more. 1,100+ commits! Release notes 👉 github.com/apache/pinot/rele…
Apache Pinot retweeted
Recorded a fantastic episode with Ankit Sultana, PMC at @ApachePinot and SWE at Uber.
We discussed Pinot’s architecture, integration with Kafka, Replication, Segment structures, query engines and much more.
Good news: we also captured on excalidraw.
Stay tuned! Releasing next week.
Subscribe here and support deep technical systems discussions: youtube.com/@thegeeknarrator
Apache Pinot retweeted
Excited to share a key Grocery use-case we had onboarded to @ApachePinot. It powers internal and external user facing tools while handling 100k+ updates per second across billions of rows.
Outside of the usual analytics workload (aggregations, etc.), with the Lucene powered Text Index, we were also able to enable user-facing search use-cases.
Discover how @Uber used Apache Pinot™ to build a real-time index on its massive inventory of billions of items to power search use cases, internal tools, and operational workflows.
read more: uber.com/blog/blazing-fast-o…
#UberEngineering #UberEng #ApachePinot
Apache Pinot retweeted
Did you know there is an @ApachePinot 🍷 connector?
It takes about 5 minutes to connect to, then you can start building dashboards and exploring your data however you want.
buff.ly/mZOQL9r
10 more minutes to go !
GPT‑5 drops at 10 a.m. PST today - Stop of the world event !
Meanwhile, Pinot engineers are clinking glasses.
Exciting days for the Apache Pinot community !
@sama
One of the key highlights from the recent meetup was hearing the team from @porterit_ talk about their journey with @ApachePinot
I was so impressed with how they evaluated their options: they looked at all the different architectural patterns to find the right way to solve for real-time analytics.
In the end, they chose the winning combo: me + Kafka. You love to see it.
Huge kudos to them for sharing their insights with the community. The entire Apache Pinot community is wishing you the best as you evolve your platform to deliver an amazing experience for your users!
Do not forget ! We are getting together today @ @meeshotech @Meesho_Official HQ , 10:30 AM Saturday IST.
Cant wait to hear what folks have built using Apache Pinot.
There’s no better place to talk about scale than sitting inside Meesho’s office with folks from @Meesho_Official , @porterit_ , @AngelOne , and @startreedata all in the room, breaking down how they do real-time analytics with Apache Pinot.
If you care about speed, scale, and systems that hold up under load, this is where you want to be.
These are the folks who quietly keep petabyte-scale systems running, where every decision maps to real business impact.
🗓️ RSVP closes soon, don’t miss it.
meetup.com/apache-pinot-bang…
There’s no better place to talk about scale than sitting inside Meesho’s office with folks from @Meesho_Official , @porterit_ , @AngelOne , and @startreedata all in the room, breaking down how they do real-time analytics with Apache Pinot.
If you care about speed, scale, and systems that hold up under load, this is where you want to be.
These are the folks who quietly keep petabyte-scale systems running, where every decision maps to real business impact.
🗓️ RSVP closes soon, don’t miss it.
meetup.com/apache-pinot-bang…
Got a question about a specific PR or want to become a Apache Pinot committer ?
Join us for Apache Pinot Contributor Call #3 - Hosted by @xiangfu0 & Robert Zych
Agenda:
8:30 AM PT – Graceful Node Replacement
In cloud-native environments, infrastructure changes are constant. For a stateful, distributed system like Pinot, node replacement is a critical challenge.
Xin Gao will walk through the mechanisms that enable seamless, zero-downtime node replacement—covering recent contributions and production-grade automation strategies.
9:00 AM PT – Timeseries Engine GA: Features & Roadmap
Shaurya Chaturvedi will share progress toward General Availability for Pinot’s new Timeseries Engine. This includes:
A brand-new query builder UI in the Controller
A redesigned Broker-compatible API
Native support for standard authentication
Join the #pinot-contributor-calls channel on the Apache Pinot Slack to RSVP - slack.pinot.live/
Cant wait to see you all there !
Apache Pinot retweeted
How did LinkedIn use a sketch algorithm in Apache Pinot to achieve:
• an 88% reduction of data (1TB → 120GB) 🔥
• improve data freshness 50%?
• 5x lower p95 latency?
🧵 In this thread, you'll:
• learn how to compute audience intersection sizes
• see some good memes 😁
Nailing complex concepts with simple clarity, Tim just gave a masterclass on Apache Iceberg and its spec.
Tim loves open source.
He loves Kafka. He loves Pinot.
Today he’s here talking about both.
The community’s buzzing
what will they build next?
Very excited for another meetup tonight, the community is excited to talk about Iceberg, Tiered Storage, Real-time & obviously myself (Apache Pinot)
And I started the week by adding a brand new @metabase driver to my repo.
Monday blues
Click to query
Pinot on call
Metabase listens
Curiosity wins
Thank you for the contribution @xiangfu0
github.com/startreedata/meta…
Did you know?
You don’t have to read docs to understand me.
You can read my code , easily - on DeepWiki.
Just be careful: You might end up admiring my creators more than me.
Am an engineering marvel !
deepwiki.com/apache/pinot
#apachepinot
Apache Pinot retweeted
Try joining the Apache Pinot Slack community; there are experienced folks who can help ! slack.pinot.live
Thank you @Uber @LinkedIn @startreedata & most importantly the developers and user community of Apache Pinot for making last night meetup a grand success.
OpenTelemetry → Jaeger → Kafka → Pinot → Query Gateway. Uber pulls 100+ traces/sec with P99 < 1 s via Scatter-Gather V1. Real-time all the way through.
#ApachePinot #RealTimeAnalytics #StreamingData #Observability #DistributedTracing #DataInfrastructure #ApacheKafka @uber
Now it’s Praveen from @LinkedIn talking about Predictable Query Latencies during data refresh and host restarts.
#apachepinot @Uber #meetup
Some great discussions with the community.
Scale, reliability, ingestion patterns @Uber HQ
