Systems | Development | Analytics | API | Testing

Episode 11: The future of data lakes: Open table formats, metadata and AI | AWS

Paul Meighan, Director of Product Management at AWS, shares how enterprises are increasingly looking for ways to integrate more data sources in their environment — especially with data lakes. From turning S3 buckets into databases to establishing better metadata layers, Meighan explores the rapid evolution of data lakes alongside data warehouses. He also explains the pivotal role AI, ML and GenAI workloads and applications will play in large metadata environments, driving innovative analytics and business insights.

Special Episode: How to make generative AI a reality | Capgemini

George Fraser, CEO of Fivetran, Bob Muglia, former CEO of Snowflake, and Steve Jones, EVP of Capgemini discuss the challenges and solutions to creating mature, production-ready generative AI models. It’s not just about algorithms or data — success lies in effective data management.

How modern query engines make governed data lakes accessible | Fivetran & Starburst

George Fraser, CEO of Fivetran, and Justin Borgman, CEO of Starburst, dive into the competitive landscape and evolution of modern query engines and data lakes. Discover how Starburst’s Trino engine and open table formats like Iceberg drive agile, scalable data solutions for AI innovation while enabling governance and other capabilities normally associated with data warehouses.

Special Episode: Data lakes drive next-gen AI infrastructure | MotherDuck

Get ready to think differently about modern data architectures as we sit down with George Fraser, CEO of Fivetran, and Jordan Tagani, CEO of MotherDuck, to explore recent evolutions of data management. They discuss MotherDuck’s innovative new connector built with Fivetran’s Partner SDK, reveal why local data processing on modern laptops is disrupting cloud-based processing and share insights on deploying AI workloads on data lakes.

Fivetran Demo: Rethinking RAG-based Gen AI with Fivetran, Snowflake, and private, structured data

Imagine building your own personalized California wine country visit assistant based on a private, structured dataset and a RAG-based approach to Gen AI. This video walks you through the process step-by-step. First, you’ll learn how to use Fivetran’s automated data movement platform to replicate a custom, structured winery and vineyard visit dataset (wineries and vineyards across all California wine country regions) from a relational database source to the Snowflake Data Cloud.