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§ Private Profile · 640 W California Ave Sunnyvale, California 94086, USA
Upsolver is a technology company.
Upsolver provides a self-serve, low-code/no-code platform designed for ingesting and transforming both streaming and batch data. The company’s core offering enables organizations to rapidly create data products, supporting diverse applications such as customer 360, real-time analytics, and AI feature engineering. Its technology streamlines data preparation for analytics and artificial intelligence, ensuring high-quality, query-ready data for data lakehouses built on open formats like Apache Iceberg, often leveraging SQL and automation to manage continuous data pipelines.
Founded in 2014 by Ori Rafael and Yoni Eini, Upsolver emerged from the founders' extensive backgrounds in data and database systems. Their initial insight stemmed from observing the complexities and resource demands of traditional data lake engineering. This led them to develop a simplified, automated approach that bypasses the need for extensive custom coding and orchestration, making advanced data capabilities accessible to a broader range of users.
Upsolver serves leading enterprises across various industries, empowering them to construct modern data architectures. The company's long-term vision is to accelerate data-driven initiatives by offering a comprehensive solution for low-latency data ingestion and optimization within open lakehouse environments, ultimately helping organizations unlock significant value from their data assets into the future.
Upsolver has raised $41.0M across 3 funding rounds.
Upsolver has raised $41.0M in total across 3 funding rounds.
Upsolver has raised $41.0M across 3 funding rounds. Most recently, it raised $25.0M Series B in April 2021.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Apr 1, 2021 | $25M Series B | Scale Venture Partners | Battery Ventures, B Capital Group, Canaan Partners, Hanabi Capital, Jerusalem Venture Partners, South Park Commons, TEN Eleven Ventures, Vertex Ventures, Dmitry Dakhnovsky, Harris Barton, Jerusalem Venture Partners, Vertex Ventures US, Wing Venture Capital | Announced |
| Jun 1, 2020 | $13M Series A | IN Rhee | Battery Ventures, B Capital Group, Citi Ventures, General Catalyst, Hanabi Capital, Jerusalem Venture Partners, Mayfield, OWL Rock Capital Partners, South Park Commons, TEN Eleven Ventures, Vertex Ventures, Frederic Kerrest, Harris Barton, Michael FEY | Announced |
| Dec 1, 2015 | $3M Seed | — | Jerusalem Venture Partners, Journey Ventures, TEN Eleven Ventures | Announced |
Upsolver has raised $41.0M in total across 3 funding rounds.
Upsolver's investors include Scale Venture Partners, Battery Ventures, B Capital Group, Canaan Partners, Hanabi Capital, Jerusalem Venture Partners (JVP), South Park Commons, Ten Eleven Ventures, Vertex Ventures, Dmitry Dakhnovsky, Harris Barton, Jerusalem Venture Partners.
Upsolver is a technology company that builds a no-code data lake engineering platform for agile cloud analytics, transforming raw big data into structured, analytics-ready tables.[1] It serves data practitioners and organizations like Cox Automotive, Wix, AppsFlyer, ironSource, and Sisense, solving the complexity of data lake engineering by automating transformations via a visual SQL IDE and execution engine, avoiding vendor lock-in with open formats like Apache Parquet compatible with engines such as Trino, Athena, Snowflake, and Redshift.[1] The platform targets high-scale workloads including big data, streaming, and AI, enabling cost-effective, agile analytics without extensive Spark hand-coding.[1][4] Upsolver has shown strong growth, tripling revenue in 2020 post-Series A, raising a $25M Series B (total $42M from investors like Scale Venture Partners, Vertex Ventures US, Wing Venture Capital, and JVP), and expanding its team, go-to-market, and innovation.[1]
Its mission is to simplify data processing, turn engineering-intensive data lakes into easy-to-use repositories serving 50X more data professionals, and unlock insights from continuously generated production data for businesses of all sizes.[2][5][6]
Upsolver was co-founded in 2014 by CEO Ori Rafael and CTO Yoni Eini, both experienced database engineers frustrated by the shift from simple SQL workflows to month-long Spark configurations for cloud analytics.[1] The idea emerged from their firsthand pain: storing affordable cloud data without vendor lock-in proved far more complex than anticipated, inspiring a platform that automates data lake best practices.[1] Early traction built through marquee customers and culminated in a $13M Series A, followed by the $25M Series B in 2021 amid revenue tripling in 2020.[1] Headquartered in Silicon Valley with global operations in North America, Europe, and Israel, Upsolver evolved from a Spark alternative to a self-serve ingestion service for streaming and AI workloads.[1][4]
Upsolver rides the explosive growth of cloud data lakes and real-time analytics, fueled by surging data volumes from streaming sources, AI/ML demands, and the shift from rigid warehouses to open, cost-efficient lakehouses.[1][4] Timing aligns with cloud adoption booms post-2020, where enterprises seek agility without Spark's overhead or vendor silos—market forces like AWS/GCP/Azure expansions and open table formats (e.g., Iceberg, Parquet) amplify this.[1][3] It influences the ecosystem by democratizing data engineering, empowering non-experts in sectors like automotive (Cox), adtech (AppsFlyer), and analytics (Sisense), while boosting query engine interoperability and reducing TCO for big data pipelines.[1][2]
Upsolver is poised to capitalize on AI-driven data pipelines and lakehouse maturation, expanding into generative AI ingestion and zero-ETL trends with further platform innovations and global scaling.[1][4] Rising streaming data from IoT/edge and regulatory pushes for open data will shape its path, potentially evolving it into a core middleware for enterprise analytics stacks. As cloud analytics demand surges, Upsolver's no-lock-in automation positions it to redefine agile data transformation, building on its revenue momentum and investor backing to challenge incumbents. This reinvention of cloud data lakes started with founders' Spark frustrations—now it's unlocking enterprise data wealth at scale.[1][5]