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Daniel Malamud
VP Sales at RelationalAI
Daniel Malamud is currently serving as VP Sales at RelationalAI, a position he has held since July 2024.1 In this role, he focuses on operationalizing business knowledge through the company's Knowledge Graph Coprocessor within the Snowflake data cloud.1
Prior to joining RelationalAI, Daniel's career included several notable positions:
- Regional Vice President of Sales for Enterprise East at dbt Labs (June 2022 to February 2024)1
- Regional Director of Sales for Enterprise NYC at Snowflake (April 2018 to June 2022)1
- Various sales roles at Sisense, Bitly, Amazon, and Mimeo1
Daniel holds a degree from the University of Vermont and attended Academies @ Englewood.1 His LinkedIn username is dmalamud, as confirmed by his recent LinkedIn activity.23
Most recently, on January 16, 2025, Daniel posted on LinkedIn about an open position for an Associate General Counsel at RelationalAI, indicating his ongoing involvement with the company.3
Highlights
We're solving really big problems here at RelationalAI! This article written by SiliconANGLE & theCUBE does a great job outlining what our CEO Molham Aref and the rest of our team is so excited about.
“Because the relational databases at the time were not powerful enough and scalable enough and their pricing models were such that they were too expensive to run the business logic, as a workaround, we pulled that logic out, we implemented by hand procedurally, because we didn’t have the relational language that could express it declaratively."
“We had to live with this two-brain architecture, where the database had no idea what the application logic was doing. And the application logic didn’t understand databases.”
The goal is to move toward a common model where data and business logic are integrated into a unified framework. This approach simplifies the architecture, reduces redundancy and facilitates better decision-making. Subsequently, a gradual refactoring of legacy applications will occur, bringing forth a modern, data-centric framework, without the need for a disruptive big bang rewrite.
It's my third week here at RelationalAI and have been learning a lot about the innovative ways our platform has tackled challenging analytical use cases.
Our very own Lizi Vermaas-Viola put together a great demo on how RelationalAI handles fraud detection on Snowflake. With any fraud detection implementation, accuracy and adoption are two key cornerstones of success. That's why combining our unique approach to deploying graph analytical capabilities directly on top of your relational data and leveraging some of the algorithms we provide to our users in our platform (in this case, weakly connected components) makes for a winning combination.
Click the link for an implementation walkthrough: https://lnkd.in/eDiQK3yF
Ping me anytime if you have any questions or are interested in learning more!
