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Chad Sanderson is passionate about data quality and fixing the muddy relationship between data producers and consumers. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp.

Huge thank you to @amazonwebservices for sponsoring this episode. AWS - https://aws.amazon.com/

MLOps podcast #226 with Chad Sanderson, CEO & Co-Founder of Gable, The Rise of Modern Data Management.

// Abstract

In this session, Chad Sanderson, CEO of Gable.ai and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making.

// Bio

Chad Sanderson, CEO of Gable.ai, is a prominent figure in the data tech industry, having held key data positions at leading companies such as Convoy, Microsoft, Sephora, Subway, and Oracle. He is also the author of the upcoming O'Reilly book, "Data Contracts,” and writes about the future of data infrastructure, modeling, and contracts in his newsletter “Data Products.”

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Timestamps:

[00:00] Chad's preferred coffee

[01:28] Takeaways

[01:38] Changes after a year

[05:13] Implementing data pipelines

[06:10] Wrong data pipelines Implement

[10:00] Promote book visibility strategies

[14:22] Data Detective Dashboard

[25:07 - 26:09] AWS Trainium and Inferentia ad

[27:56] Data lineage tracking solution

[34:30] Putting conditions

[38:47] Magic solution for productivity

[41:41] Surprising features

[43:55] Upstream all the way

[46:03] Data integrity challenges

[48:53] Other surprising features

[51:45] Interfering with DevOps & Data

[54:12] Data security and value

[56:15] AI Quality In-Person Conference