Data Council 2025 Call for Proposals 2025

TECHNICAL DEPTH

Dive into advanced topics from a deep, technical perspective.

REAL LIFE EXAMPLES

Share solutions
and hard-fought insights
from the trenches.

INNOVATIVE SYSTEM DESIGN

Showcase novel
approaches to building
robust systems.

About Our Tracks

01 Data Eng & Infrastructure

Data Engineering & Infrastructure focuses on modern data engineering workflows, storage systems, design patterns and more. Themes in this track are centered around the most important pieces of the data pipeline workflow: data-ops, data quality, ingest & ETL/ELT, monitoring, metadata and other issues pertaining to the modern data stack.

02 AI Engineering

This track goes deep into the skills and toolchain every AI engineer must know to build, deploy and scale AI systems. Example topics covered include model selection & evaluation, RAG, vector & graph search, fine-tuning, agent orchestration, data prep and testing/deployment/monitoring. Learn how to build robust, scalable AI systems using the latest tools in AI engineering.

03 Data Science & Algos

The Data Science & Algorithms track is focused on helping data science professionals be more effective in their role. We discuss helpful algorithms, modern research and data science frameworks & methodologies that can be useful in data science functions across the enterprise.

04 Foundation Models

Focuses on the rapidly evolving landscape of foundation models, such as large-scale language models, multimodal models, and AI systems that serve as the backbone for a wide range of AI applications. Sessions will explore the architecture, training techniques, and deployment of these models, along with their implications across various industries.

05 Analytics & BI

The Analytics track focuses on tools and techniques for data analysts, covering topics such as Business Intelligence (BI), customer analytics, A/B testing and data visualization. You’ll learn about how top teams are solving their analytics challenges and discover the best new tools in the process.

06 MLOps & Platforms

This track focuses on the engineering behind existing and novel machine learning systems, frameworks and tooling. You’ll learn about topics such as data preparation, feature engineering, model quality & monitoring, ml-ops, and best practices in generalizing ML workflows.

07 GenAI Applications

This track explores the practical applications of GenAI across industries. We discuss how orgs can innovate in content creation, automation, customer engagement, and product development. Sessions will delve into real-world case studies of AI architectures, best practices for integrating GenAI into existing workflows and the ethical considerations surrounding its use.

08 Databases

This track dives into the evolving world of databases, covering both traditional relational systems and emerging database technologies designed to handle the complexities of modern & unstructured data. Topics will include the latest trends in database architecture, performance optimization, distributed databases, and data management for AI and analytics workflows. Attendees will learn how to design, scale, and secure databases to meet the demands of high-performance applications, data-driven decision-making, machine learning & AI. This track is ideal for database engineers, data architects, and anyone interested in mastering data infrastructure in today’s dynamic tech landscape.

09 AI & Data Culture

Data Culture is a place for data leaders to share stories and insights on how they have built vibrant, cross-functional, and collaborative spaces for their teams. Whether you are building a data team from scratch, establishing a strong data culture within your organization or rallying a global network of developers around your software, you’ll have plenty to learn from our speakers who have done it all.

10 Lightning Talks

The Lightning Talks track is composed of 15min, bite-size sessions from various startups sharing lessons learned & best practices of their fast-growing companies. You can learn about new tools & approaches that startups use, cutting-edge open source projects, and data teams' lessons learned from supporting their company growth.

Wes McKinney: The Future Roadmap for the Composable Data Stack

Wes reviews the progress we have made in the last 10 years developing composable, interoperable open standards for the data processing stack

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Dhruv Singh: Strategies for Assessing LLMs in Real-World Applications

Dhruv discusses practical solutions that will unlock faster iteration & more safety in GenAI, such as using tiny evaluators in an online setting & making efficient use of human feedback offline.

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DJ Patil & Joey Gonzalez: Why it Takes Billions

DJ Patil and Joey Gonzalez discuss how to navigate the AI landscape with OpenAI, Google, Nvidia and Everyone Else with Billions to Spare

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Dan Mejia & Michael Bullington: the Journey to FigJam AI

Learn about the unique development process of FigJam AI, from the inception at a hackathon to fine-tuning for specific use cases.

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Pete Hunt: Why You’ve Been Thinking About the Wrong DAG the Entire Time

Pete explains how declarative orchestration goes beyond improved developer ergonomics: it has profound consequences for the entire data platform.

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Jordan Tigani: Big Data is Dead

Jordan makes the case that the era of Big Data is over. Now we can stop worrying about data size and focus on how we’re going to use it to make better decisions.

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