The Data Engineer's role amidst the rise of big data, cloud computing, and AI-driven analytics has shifted. This panel chat explores the ever-changing landscape of essential skills and the automation of outdated ones. With a myriad of architectural options available, we'll dissect how organizations navigate the complexities to tailor solutions to their specific needs. Let's unravel the intricacies of building scalable data systems, pinpointing common breakpoints and strategies for efficient scaling. Come along as we delve into in constructing the foundation of the data-driven future.

ABOUT THE TALK: If you squint, the data warehouse-centric analytics stack can look a lot like mainframe-era centralized control: shared distributed compute, some sandbox space if you are lucky, and no way to work locally that's equivalent. And it's been getting expensive! Yet the major vendors appear to be converging on features. Is this "The End of History" for OLAP? Your laptop is incredibly powerful and tragically underutilized. It has better I/O than servers from just a few years back, and with a solid analytic engine like DuckDB, it can handle surprising workloads.

In this talk Nicholas Ursa will share what makes DuckDB fast and show how far single node scale-up can take you. He will also show some ideas Motherduck is testing that blur the line between local and remote systems and shift some of that centralized control back to users.

ABOUT THIS SPEAKER: Nicholas Ursa is an Engineer at Motherduck. He led data engineering teams at The New York Times and better.com after cutting his teeth in ad tech.

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