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
Beyond the Forklift: Unlocking Revenue-Critical Workloads on Iceberg
·OnlineOnline13 attendees from 10 groupsTo attend, register here.
Apache Iceberg has become the open standard for modern data platforms, yet most adoptions approach migration as a forklift. Governance improves, storage is standardized, and BI workloads run reliably; but the most demanding analytics, the ones closest to revenue and customer experience, are rarely considered in scope for Iceberg.
Those SLA-bound data products—embedded dashboards, fintech merchant cash flow views, surge pricing, fraud detection, incident response—don’t simply go away when Iceberg isn’t built to serve them. If the platform isn’t engineered for deterministic p95/p99 latency and high-concurrency guarantees, the requirement resurfaces elsewhere. What was intended to be a shared system of record becomes relegated to a feeder layer, pushing data into downstream systems where it is re-stored, re-governed, and re-queried outside of Iceberg.
This session outlines a different model: Iceberg as the open system of record, paired with a purpose-built execution layer that enforces deterministic SLAs directly on Iceberg tables, eliminating shadow stacks and restoring architectural coherence for revenue-critical insights.
Past events
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