Kishore Gopalakrishna
Co-Founder
StarTree
StarTree provides a platform-as-a-service built on Apache Pinot, enabling real-time analytics with sub-second query response times on petabyte-scale data. This solution allows businesses to efficiently handle high concurrency demands while minimizing costs associated with data processing and analysis.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Software Only
Updated 11 months ago
Funding
Raised to date
$75.2MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 3 rounds
GC$47M · Series B
Announced Sep 2022
Disclosed cumulative: $75M
3/3
RoundDateAmountValuationInvestors
Founders
Product
Problem
Many businesses struggle to analyze large, rapidly changing datasets in real-time, leading to delayed insights and missed opportunities. Existing analytics solutions often fail to deliver the speed and scalability required to handle high data volumes and concurrent user queries efficiently. This can result in slow response times and increased infrastructure costs.
Solution
StarTree provides a platform-as-a-service (PaaS) built on Apache Pinot that enables real-time analytics with sub-second query response times on petabyte-scale data. The platform is designed to handle high concurrency demands, allowing businesses to efficiently analyze data from various sources and deliver insights to millions of users. StarTree simplifies the process of building user-facing analytics applications by providing a fully managed service that eliminates the need to manage underlying infrastructure. By leveraging optimized data processing structures, StarTree minimizes delays and keeps costs low, regardless of the amount of data being analyzed.
Target Audience
The primary target audience includes businesses across various industries such as financial services, delivery and ridesharing, gaming, healthcare, retail, and HR that require real-time analytics for user-facing applications and internal decision-making.
Features
- Sub-second query response times on petabyte-scale data
- High concurrency support for millions of users
- Real-time data ingestion from streaming and batch sources
- Auto-sync with various data sources, warehouses, and lakehouses
- Fully managed Apache Pinot service with self-serve provisioning
- Flexible deployment options and enterprise support
- Optimized data processing structures for minimal delays
- Integrations with popular data streaming and batch processing systems