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Most databases designed for the cloud follow a client-server model, but what happens when you need to scale an in-process, embeddable database like DuckDB? This talk dives into the engineering challenges of building a cloud-native analytics platform from an embedded database. Through real-world lessons from building MotherDuck, we’ll explore the trade-offs, design decisions, and practical insights that apply to software engineers working on scalable, high-performance data systems. Whether you’re building a database, optimizing cloud infrastructure, or working on large-scale analytics, this talk will give you concrete takeaways for architecting reliable, cloud-native systems.
Speaker
Stephanie Wang
Staff Engineer @MongoDB | Previously Founding Engineer @MotherDuck and Worked on @Google BigQuery
Stephanie is a Staff Engineer at MongoDB, where she focuses on designing and building scalable, high-performance database systems. Previously, she was a founding engineer at MotherDuck, where she played a key role in shaping its cloud-native analytics platform built on DuckDB. Prior to that, she worked on Google BigQuery, specializing in BigQuery client libraries and connectors.
Stephanie is passionate about developing solutions that enhance performance, scalability, and usability. She is particularly interested in the challenges of cloud-native data systems and ensuring that complex infrastructure remains robust and efficient.
