Chicago Data Night: James Nowell (EZBot AI Company)

Please join us for our monthly Chicago Data Night, cohosted by Drive Capital, ChiData and the DSI. Practitioners, academics, and aficionados within the Chicago area are all invited to be part of a community at the intersection of industry and academia, brought together by a mutual interest in data. Each month, guest speakers will cover a specific data-related topic.

Hors d’oeuvres and drinks will be provided. Admission is free, and we strongly encourage an RSVP to attend. Please note our new event location at Drive Capital!

Meeting Location
Drive Capital, Fulton East Building
215 N Peoria St
Chicago, IL 60607

Abstract: In this talk, we’ll explore how thinking in Events and Domain-Driven Design can make you a stronger data producer and consumer. We’ll use ezbot.ai’s data pipeline as an example of this paradigm’s usefulness.

For most people, data means Rows and Columns in Tables. It means Entity-Relationship diagrams, SQL, and Joins. While this data modeling can be fantastically useful, it’s easy to “lose” lots of valuable information.

Rows frequently represent Entities. Entities may “change” over time; a User may move and change their Address. They may change their name or email. A Return may negate an Order. Without careful thought, these changes may cause an irrecoverable loss of information.

Enter Events: events are Facts about things that happened at a specific time. Events can describe changes to Entities, relationships, data points, and more. The State of an Entity can be reconstructed from the history of all of it’s relevant events. Treating Events as the Quanta of the data world can help ensure you never lose data and can always reconstruct information.

Using Events as a conceptual framework and Domain-Driven Design to explain how Entities and Events interact can help all data users better understand their domain.

Bio: James Nowell is CTO and Co-Founder of EZBot AI Company, building AI tools to optimize web user experiences. He is an expert in data engineering and machine learning operationalization, with over a decade of experience building engineering teams from the ground up, architecting low-latency big data streaming services, and leading large software organizations. Most recently, he was the Principal Engineer on Peloton’s Data Platform team. He helped build a modern data-mesh architecture that enabled rapid AI and ML development and low-latency business intelligence. His data experience has covered:

* Supply Chain
* Ground, Ocean, and Infrastructure Telematics
* Marketing
* Consumer Products
* Defence

He has a passion for low-latency streaming analytics, complex search and aggregation problems, and building efficient, maintainable, scalable data systems.