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Daniel Malamud
VP Sales at RelationalAI
Daniel Malamud is the Vice President of Sales at RelationalAI, a company that specializes in relational knowledge graph management systems.13 He joined RelationalAI in August 2024, marking his third week with the company as of August 14, 2024.4
Professional Background
Prior to his role at RelationalAI, Daniel held several positions in the tech industry:
- Head of Sales, Enterprise East at Fishtown Analytics (2022 - present)5
- Regional Vice President of Sales at an undisclosed company (2022 - 2024)5
Education
Daniel Malamud attended the University of Vermont, though specific details about his degree or graduation year are not provided in the available information.2
Current Role
As VP of Sales at RelationalAI, Daniel likely plays a crucial role in:
- Helping businesses solve problems and realize their potential through RelationalAI's solutions2
- Promoting RelationalAI's platform, which is described as innovative in tackling challenging analytical problems4
- Leading sales strategies for a company that aims to build the world's fastest, most scalable, and most expressive relational knowledge graph management system6
Daniel's LinkedIn profile can be found under the username "dmalamud", which aligns with the information provided in the query.2
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!
