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Upsolver's mission is to accelerate the delivery of big data
to the people who need it.
To achieve this, we invented cloud-native technology for ingesting batch and streaming data with built-in quality and observability. By addressing data quality issues as data arrives, Upsolver eliminates the time and complexity of delivering trustworthy analytics, without compromising on data freshness or scale.
Upsolver is a closely-knit group of data engineers and infrastructure developers who are obsessed with making big data ingestion pipelines so easy to create that any data practitioner could do it, even when dealing with streaming and complex data at big data scale. We built the first version of Upsolver for our own use, and enjoyed eating our own dog food so much that we decided to bring this value to every company doing analytics in the cloud. We now claim numerous companies as customers spanning industries as diverse as oil & gas to gaming, feeding both operational analytics and customer-facing data products.
What we lost in the transition to big data
In the early days of Upsolver we recognized that something important was lost in the transition to big data. Decades ago, databases were invented to abstract away the complexity of coding over raw file systems, letting data teams “speak” in schemas and SQL, which let them transform data more easily and iterate faster.
Upsolver’s goal is to re-invent the simplicity of a database on top of the tremendous economic and “power at scale” advantages of the cloud. That is why we focused on either no-code, or low-code using SQL as the lingua franca for designing pipelines, and for data lake transformations why we automate all of the plumbing that takes most of the time.
By eliminating any compromise across the dimensions of power, simplicity and affordability, we are transforming the data pipelines that power cloud analytics workloads. Data engineers who use Upsolver become much more productive, delivering data pipelines that normally would have taken months, in hours. Plus, SQL-savvy data consumers such as data analysts, analytics engineers and data scientists now can ingest and transform complex raw data, without having to become data engineers.
Realizing the potential of the cloud
This will widen the aperture of analytics, unlocking data that heretofore has been difficult to analyze. System logs, clickstreams, IoT sensor feeds, and other continuous data sources will be blended with static and historical data to create query-ready data sets that feed reporting or ad hoc queries with unmatched data freshness, as well as customer-facing data products.
Yoni is a technologist specializing in big data and predictive analytics algorithms. He served in several senior technology roles, including as CTO of the data science division in the IDF’s elite technology intelligence unit. Yoni holds a BA in Mathematics and Computer Science which he started before he was 16.
Peter Wagner is a Founding Partner of Wing. He focuses on the Modern Enterprise, seeking out founders and opportunities that have the potential to transform or create explosive markets. Peter has led investments in dozens of early stage companies, many of which have gone on to complete IPO’s or successful acquisitions. Since leaving Accel in 2011 and founding Wing, Peter invests in and helps to build exciting early stage enterprise technology venture
Gadi has over 20 years of experience in the Hi-Tech industry, in management positions delivering products to global markets. Gadi sits on the board of several of JVP’s companies including Teridion, Iguaz.Io, Vicarius, Valid, Compira Labs, Copilot, Datarix, and Cyberfish. Gadi has an Executive MBA from Bar-Ilan University, Israel and a BA in Economics from Ruppin College, Israel.
Upsolver’s innovative streaming data technology is rapidly changing the big data landscape.
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