Upsolver
SQL Data Pipelines for Cloud Data Lakes
Company Overview
Snapshot
Founded in March 2014 by Ori Rafael and Yoni Einy, Upsolver grew to 11–50 employees. The company raised $42.5M across 4 funding rounds from 10 investors. In January 2025, Upsolver was acquired by Qlik.
Business overview
Upsolver provides a cloud-native platform designed to simplify data lake ingestion, storage, and ETL processes. Its core technology enables data practitioners to create continuous analytics-ready data pipelines, significantly reducing the time typically required for hand-coding and orchestrating Spark. The company operates in the Business Software sector, serving markets that leverage big data, deep learning, machine learning, SaaS, artificial intelligence, business intelligence, and IT management, particularly focusing on data lakes. Upsolver is an Advanced Technology Partner within the Amazon Web Services (AWS) Partner Network, with its platform available via the AWS marketplace.
Strategic signal
In May 2022, Upsolver was featured in a Datanami article discussing the potential for data lakehouses to lead to warehouse-style lock-in. This highlights the company's relevance in ongoing industry discussions about data architecture and its strategic positioning within the evolving data management landscape, signaling its role in shaping future data infrastructure solutions.
Log in to access full profile ›Company Intelligence Q&A
- When was Upsolver acquired?
- Upsolver was acquired by Qlik in January 2025.
- Which investors participated in Upsolver's April 2021 funding round?
- In April 2021, Upsolver received funding led by Scale Venture Partners, with participation from.
- What was the total capital raised by Upsolver?
- Upsolver raised a total of $42.5M across its funding rounds.
- Which investors participated in Upsolver's June 2020 funding round?
- In June 2020, Upsolver secured funding led by JVP, with participation from Jeff Rothschild.
- What is Upsolver's primary focus?
- Upsolver focuses on providing a cloud-native platform for SQL data pipelines, abstracting the engineering complexity of data lake ingestion, storage, and ETL for continuous analytics-ready data.