The database for customer-facing analytics at scale

Streaming Stats, Stickier Sites, Simpler Stacks
Sub-second latency. 10K, 20K, even 80K+ concurrent queries. Finally, a system that keeps up.

When it’s customer-facing, every millisecond matters

Internal dashboards can afford delays. Customer-facing apps cannot. External users expect answers instantly. Slow metrics mean one thing in customer-facing apps – customers don’t stick around.

But scale makes this hard. Supporting tens of thousands of concurrent queries while still delivering sub-second latency is traditionally impossible without brittle workarounds.

The old pattern? Pre-aggregate data, move it into a separate serving layer, and brace yourself for endless pipeline rewrites every time requirements change. The result: complexity, cost, and fragility.

StarTree changes the equation…

Real-time analytics no longer means expensive

For years, “real-time” meant bending a traditional data warehouse or lake into something it was never designed to do — with ballooning compute costs and endless serving-layer complexity.

StarTree flips this equation. When real-time is the core design, compute costs fall dramatically: queries resolve faster, indexes reduce overhead, and concurrency scales without runaway spend.

Storage, too, no longer carries a tradeoff. With Iceberg and S3 integration, data lives cheaply where it belongs — yet insights are still delivered with sub-second SLAs.

The result: faster apps, simpler architectures, and significantly lower TCO.

Performance + simplicity + efficiency for customer-facing insights

Extreme Concurrency
Serve 10K, 20K, even 80K+ queries per second without breaking a sweat.
Sub-Second Latency
Insights load in milliseconds, keeping your customers engaged.

Fewer Hops, Less Movement
Query streams and history in place—no pre-aggregation, no shuffling data through extra serving layers.
Cost-Efficient by Design
Engineered to cut compute and I/O costs while scaling seamlessly.

Purpose-built for customer-facing apps

StarTree, powered by Apache Pinot, delivers what traditional platforms can’t: sub-second query latency and extreme concurrency in the same system.
Instead of moving data into brittle serving layers, StarTree plays the data where it lies — whether it’s in streams, databases, or object stores like Iceberg and S3 — cutting out costly pipelines while still meeting the most demanding SLAs.
At the core are Pinot’s innovative indexing techniques (star-tree, inverted, range, text, JSON) that both slash query latency and allow apps to handle 10K–80K+ queries per second without compromise.
Meanwhile, real-time upserts at scale ensure metrics stay accurate and fresh, even as millions of new events stream in.
COMPLIMENTARY REPORT

MIT Technology Review
Transform Customer Experience with Embedded Real-Time Analytics

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