San Francisco, CA, United States of America

Saebom Kwon


Average Co-Inventor Count = 7.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations by Saebom Kwon in ETL Pipeline Technology

Introduction

Saebom Kwon is an innovative inventor based in San Francisco, CA. He has made significant contributions to the field of data processing, particularly in the development of systems that enhance the usability of ETL (Extract, Transform, Load) pipelines for non-code users. His work focuses on simplifying complex data transformation processes, making them more accessible to a broader audience.

Latest Patents

Saebom Kwon holds a patent for "Automatic data linting rules for ETL pipelines." This patent describes systems and methods that allow non-code users to create transformations for databases within ETL pipelines. The data linting system he developed extracts a schema and a data sample from a database, creating two rulesets that are combined into a final ruleset. This final ruleset is validated using the data sample and sent to the user, enabling immediate application in an ETL pipeline.

Career Highlights

Saebom Kwon is currently employed at Salesforce, Inc., where he continues to innovate in the field of data management. His work at Salesforce has positioned him as a key player in developing user-friendly data processing solutions. His contributions have not only advanced the technology but have also made it easier for businesses to manage their data effectively.

Collaborations

Some of Saebom Kwon's notable coworkers include Ignacio Agustin Manzano and Subhash Periasamy. Their collaborative efforts contribute to the innovative environment at Salesforce, fostering advancements in data technology.

Conclusion

Saebom Kwon's work in developing automatic data linting rules for ETL pipelines represents a significant advancement in making data transformation accessible to non-code users. His contributions at Salesforce, Inc. highlight the importance of innovation in the field of data management.

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