dx.one, part of Volkswagen Group, processes customer and vehicle data that supports after-sales products across the group. After migrating a petabyte-scale data platform to AWS, the team faced a familiar but difficult question: how do you know data is complete, fresh, and trustworthy when it moves through legacy systems, fragmented interfaces, and thousands of pipelines? Santiago Gomez, Principal Cloud Architect at dx.one, shares how his team approached data observability during and after the migration. He discusses the signals they rely on today, including database monitoring, custom metrics, and data quality checks, and how those signals help teams detect issues before they reach downstream consumers. The session also covers the tradeoffs dx.one is still working through: where traditional infrastructure monitoring falls short, which metrics have proven useful, and how the team plans to make data reliability more visible to both producers and consumers. Learn a practical framework for choosing meaningful data observability signals, connecting them to operational workflows, and improving trust in complex enterprise data systems.

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