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From Data Engineering to AI Engineering: Where the Lines Blur

Summary In this solo episode of the Data Engineering Podcast, host Tobias Macey reflects on how AI has transformed the practice and pace of data engineering over time. Starting from its origins in the Hadoop and cloud warehouse era, he explores the discipline's evolution through ...  Afficher plus

Malloy: Hierarchical Data, Semantic Models, and the Future of Analytics

Summary In this episode Michael Toy, co-creator of Malloy, talks about rethinking how we work with data beyond SQL. Michael shares the origins of Malloy from his and Lloyd Tabb’s experience at Looker, why SQL’s mental model often fights human problem solving, and how Malloy aims ...  Afficher plus

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Making the Turn from Data Inventory to Helpful Information with Mara Reiff, the Chief Data Officer of FreshBooks
IT Visionaries

If data is in a pool that only keeps getting deeper as data inventory is accounted for, when is the exact moment for a business leader to jump in to do something with all the accumulated information? Leaders who care about data appreciate that it’s necessary to take stock befo ...

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Mining the Golden Age of Data with Tableau’s CEO & President Mark Nelson
IT Visionaries

Mark Nelson is the President and CEO of Tableau, a company dedicated to democratizing analytics and putting data back in the hands of consumers. But while this digital pioneer ma ...

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Buying and Selling Homes Algorithmically with Opendoor’s VP of Research and Data Science, Kushal Chakrabarti
IT Visionaries

For many people, the process of buying and selling a home will undoubtedly be the most difficult decisions they will make in their lifetime. Is the price you’re paying for your home fair? Is the price you’re selling your home for an adequate sale price? For a long time, realto ...

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From Different Leadership Vantage Points: Data Drives Value but is Driven by Values
IT Visionaries

One way to think about data is that it is like rain, and it is pouring outside. Imagine c-suite executives running around in a parking lot with huge buckets trying to capture as much as they can. Afterward, they return to the office, analyze the data, and then decide what to d ...

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