StarTree, a startup building a platform-as-service version of the Apache Pinot analytics platform, today announced that it closed a $24 million series A round led by Bain Capital Ventures and GGV Capital. The company says the proceeds will be used to spur adoption of its user-facing analytics products as it ramps up its customer acquisition efforts.
Most enterprises have to wrangle countless data buckets, some of which inevitably become underused or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends. The opportunity cost of this unused data is substantial, with a Veritas report pegging it at $3.3 trillion by 2020. That’s perhaps why the corporate sector has taken an interest in solutions that ingest, understand, organize, and act on digital content from multiple digital sources.
Founded by Kishore Gopalakrishna and Xiang Fu, former engineers at LinkedIn and Uber and the original creators of Pinot (which StarTree maintains), StarTree provides a concurrent, scalable, and real-time analytics platform-as-a-service. The company leverages both batch and streaming data, letting operators, analysts, customers, and users understand and take action in real time. And StarTree claims its products can scale from thousands to millions of users with low latency.
“While Pinot itself is open source, not every company has the skills to manage, operate and configure Pinot,” a spokesperson told VentureBeat via email. “StarTree’s fully managed Pinot product offering has continued to grow during the pandemic. We have specifically seen increased interest from companies in expanding the use of analytics. In fact, a number of companies realized that the insights they gathered before the pandemic were outdated, as consumer behaviors and interests drastically changed.”
With Pinot, businesses like retailers have the ability to track supply chain and inventory changes at a very granular level, Gopalakrishna said in an interview. Fintech companies can use Pinot to provide real-time insights to their clients and individual users, while restaurants can respond to changes in demand and staff up or down in response to search and order behaviors related to their locations.
One customer, Stripe, tapped Pinot to execute “sub-second, petabyte-scale” aggregation queries over fresh financial events in its internal ledger. Meanwhile, LinkedIn used Pinot to serve more than 150,000 queries per second across over 70 user-facing apps.
“Apache Pinot revolutionized the way LinkedIn thinks about data analytics and delivers value to our members and customers through highly dimensional, near-real-time insights. Member experiences … have been enabled at scale thanks to Pinot’s performance profile,” LinkedIn chief data officer Igor Perisic said. “As the first company to use this technology, we are thrilled to see StarTree moving quickly toward commercialization so the rest of the world can reap similar benefits from the platform.”
Existing investors Charles River Ventures and LinkedIn also participated in 18-employee StarTree’s latest funding round. It brings the company’s total raised to more than $28 million.
Data analytics is the science of analyzing raw data to extract meaningful insights. Market Research Future predicts that the global data analytics market will be valued at over $132 billion by 2026. A range of organizations can use data to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. Businesses that use big data increase their profits by an average of 8%, according to a survey conducted by BARC research.
“Today companies can extract tremendous value, by expanding insights to their customers, partners, and franchisees. StarTree aims to address that need by helping progressive data-driven companies make better use of their massive and growing data,” Gopalakrishna said. “Building on the success of Pinot, we are in the unique position to democratize data by delivering fresh analytics at scale with low latency for thousands or millions of users.”
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