The premier %%virtual summit%% on data reliability, observability, and the future of trustworthy AI.

AI initiatives collapse not because of models, but because of the data beneath them. In this opening conversation, Sanjeev and Salma frame the executive challenge: how enterprises can design for reliability from ingestion to insight. Expect sharp takes on why trust is becoming the new KPI for AI success.

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dbt transformed how teams build with data; observability defines how teams trust what they build. In this fireside, Tristan and Salma explore how a trust layer is emerging alongside transformation and semantic layers — and why reliability, quality, and transparency are now essential guardrails for modern analytics.

Metadata is more than documentation, it’s the connective tissue for observability, lineage, and scale. Tomas explains how metadata-centric architectures create durable trust in modern data platforms.

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Contracts and lineage are hot topics, but what really scales in practice? Practitioners share candid lessons on designing observability that works in production environments.

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The modern data stack promised speed and flexibility, but reliability still lags behind. In this keynote, Joe Reis argues that observability isn’t a feature you add later, it’s an architectural principle you must design from day one. A pragmatic call to rethink reliability as the core of your data platform.

At Shopify, Solmaz built data products where adoption hinged on trust. In this conversation with Salma, she shares lessons on designing for reliability, explainability, and user confidence when business outcomes depend on it. From fraud detection to merchant payouts, this fireside shows why in data products, trust isn’t a feature, it’s the product.

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Most governance frameworks crumble under scale. Mahdi reveals how metadata-driven observability is making compliance and control possible without slowing teams down. Expect real-world insights into lineage, policy enforcement, and embedding trust into the data fabric.

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A multi-voice discussion on deploying observability across massive, global data supply chains, and why process change is harder than tool adoption.

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Governance used to mean gatekeeping. Today it must balance agility with accountability. In this panel, senior leaders debate how observability reshapes governance strategies, how to operationalize lineage for risk management, and how enterprises can design governance that unlocks innovation instead of stifling it.

AI is everywhere, but dependable AI is rare. In this keynote, Ben Castleton cuts through the agentic hype to show what it really takes to deploy AI systems that work in practice. Expect candid insights on pipelines, monitoring, and why “self-healing” is harder than most vendors admit.

Self-service and AI queries open new opportunities, but also risks of inconsistency and mistrust. Colin explores how governance and semantic models ensure trust doesn’t collapse as access expands.

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Airbyte CEO Michel Tricot explores how enterprises can accelerate AI adoption without sacrificing data control. Learn how sovereign data movement architectures enable AI agents to access comprehensive data across systems while maintaining security boundaries, preserving metadata, and meeting compliance requirements, unlocking AI deployment at scale

From Airflow to MCPs, Maxime outlines how infrastructure is evolving to support adaptive, trustworthy workflows at scale.

As enterprises rush into AI, scalable infrastructure must also be secure and trustworthy. Seth shares Snowflake’s perspective on aligning infrastructure, observability, and governance to support reliable AI adoption.

The summit closes with a manifesto-style discussion on what “trust by design” really means for the next decade of data and AI. Our panelists reflect on the week’s conversations, share their visions for the future, and lay out the principles that will define trustworthy data systems at enterprise scale.

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