Upsolver Raises $25M Series B Round To Reinvent Analytics On Cloud Data Lakes
Company triples revenue as the market embraces its no-code approach to making raw cloud data analytics-ready
Upsolver, the only no-code data lake engineering platform for agile cloud analytics, today announced $25 million in Series B financing led by Scale Venture Partners (Scale). Existing investors JVP, Vertex Ventures US, and Wing Venture Capital also participated in the round. As part of the investment, Ariel Tseitlin — Partner at Scale and former Head of Netflix Cloud Solutions — has joined Upsolver’s board of directors. The company also announced its free Upsolver Community Edition, an integral part of the Upsolver platform that delivers on its vision for universal access to cloud data.
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“Unfortunately, what used to take three hours using SQL turned into a month or more of hand-coding and hundreds of configurations in Spark. We created Upsolver to transform cloud analytics into an agile process.”
The new funding comes on the heels of Upsolver’s $13 million Series A and follows a tripling of the company’s revenue in 2020, including the addition of marquee customers such as Cox Automotive, Wix, and AppsFlyer. Upsolver will use the financing to aggressively build its team, scale its go-to-market engine, and drive technical innovation.
Companies are rapidly migrating their organizational data into cost-effective cloud data lakes. However, making the data valuable for analytics requires complex engineering projects that take months to complete and rely on unicorn data engineers skilled in distributed systems like Apache Hadoop or Apache Spark.
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With Upsolver, cloud analytics can be both cost-effective and agile. Upsolver addresses the complex challenge of transforming raw, big data into structured tables with a visual SQL IDE that any data practitioner can use, combined with an execution engine that automates data lake engineering to ensure high-performance. Since Upsolver is based on open source file formats like Apache Parquet, companies avoid vendor lock-in and can take advantage of a multitude of query engines such as PrestoDB, Trino, and Athena, or data systems like Snowflake, AWS Redshift, Azure Synapse Analytics, Splunk, and Elasticsearch.
“Monolithic analytics platforms are a thing of the past. Today’s organizations require a variety of analytics tools to fully capitalize on their data,” said Ariel Tseitlin, Partner at Scale. “Data lakes originally promised this variety and openness but also required a large, ongoing investment in engineering. Upsolver eliminates this trade-off. The company’s steep growth curve, top-quartile net revenue retention, and superior technology prove its leadership in the cloud data space. We’re thrilled to back the team.”
Ori Rafael (Rafael) and Yoni Eini (Eini), two database engineers, founded Upsolver after experiencing firsthand the frustration and complexity of building a cloud analytics solution using Spark. “We wanted to store data affordably in the cloud without analytics vendor lock-in,” said Rafael. “Unfortunately, what used to take three hours using SQL turned into a month or more of hand-coding and hundreds of configurations in Spark. We created Upsolver to transform cloud analytics into an agile process.”
