No-Coder Upsolver Aims to Ease Use of Cloud Data Lakes
Upsolver, the no-code data lake platform vendor, has closed a $25 million funding round this week, boosting total venture funding for its cloud analytics tools to about $42 million.
The financing round announced Tuesday (April 6) was led by Scale Venture Partners. Ariel Tseitlin, a Scale partner and former head of Netflix Cloud Solutions, joins Upsolver’s board of directors.
Also participating in the Series B round were existing investors Jerusalem Venture Partners, Vertex Ventures US and Wing Venture Capital.
Upsolver, Sunnyvale, Calif., said it would use funding for R&D as well as product introductions, including a free community edition of its engineering platform also released this week. The platform is designed to provide “universal access” to cloud-based data by making data lakes easier to use.
Upsolver’s platform is also available on the Amazon Web Services (AWS) and Microsoft Azure marketplaces.
The new funding reflects the growing market for helping companies migrate corporate data to cloud data lakes. Upsolver’s no-code approach also includes cloud analytics once data are migrated.
The company promotes its no-code approach as eliminating the engineering tradeoffs associated with the migration to cloud data lakes. Those tradeoffs include complex data pipelines required to make data available for analytics on distributed systems like Apache Spark and Apache Hadoop.
Upsolver said its no-code platform addresses challenges like transforming unstructured data into structured tables via a visual SQL integrated development environment. The IDE is aimed at data novices. That tool is combined with an execution engine to automate the engineering steps associated with migrating to cloud data lakes.
Upsolver’s engineering platform is based on open source formats like Apache Parquet that help avoid vendor lock-in while providing access to query engines such as Athena, PrestoDB and Trino. It also connects to AWS Redshift, Microsoft Azure Synapse Analytics, Snowflake and other data systems.
The no-code startup was founded by data engineers Ori Rafael and Yoni Eini to address the complexity of building cloud analytics tools using Spark. “What used to take three hours using SQL turned into a month or more of hand-coding and hundreds of configurations in Spark,” Rafael said. “We created Upsolver to transform cloud analytics into an agile process.”
“The problem with the data lake is that it’s only half a database,” Rafael added in in a blog post announcing its latest funding round. “You’re replacing a single product like Oracle with an architecture.”
Oracle “did automate an awful lot of what we now call ‘data engineering’,” Rafael added. “With the data lake, this is now the customer’s job, which has created an entire cottage industry of services, software tools and hand-coding as companies try to stitch together raw data into analytics-ready data sets.”
Upsolver’s goal is “to make the cloud data lake as easy to use as a database,” said Rafael.