Open Lakehouse + AI | Paris, FR
Open Lakehouse + AI Meetup - Monday, November 24, 6:30 PM – 10:00 PM CEST | Paris, FR
We're bringing together the open source and data engineering community for an evening focused on the latest in open lakehouse and AI architectures! 🚀 Whether you work on data infrastructure, contribute to open source, or want to dive into the future of AI and interoperable lakehouse systems, you’ll fit right in.
Don't miss this opportunity to accelerate your data journey and contribute to shaping the future of data and AI! 🌟 This event is co-located with the Forward Data Conference.
Agenda
6:30 PM: Registration, Bites & Mingling
7:00 PM: Welcoming Remarks (hymaïa)
7:15 PM: Session #1 – What's new in Delta Lake 4.0 & Iceberg v3 and Unified Data Governance with Unity Catalog
Youssef Mrini, Solutions Architect, Databricks
El Ghali Benchekroun, Specialist Solutions Architect, Databricks
7:50 PM: Session #2 – From Internal Metrics to Customer-Facing Analytics: Building a Scalable Usage Insights Platform with Delta Sharing
Alexandre Bergere, Head of Data & AI Engineer, Partners at @DataGalaxy & Data Architect freelance at @Datalex
8:25 PM: Session #3 – Beyond Data Ingestion, What Data Engineering Design Patterns for Open Lakehouse
Bartosz Konieczny, Freelance Data Engineer, waitingforcode.com
9:00 PM: Reception with dessert and beverages
10:00 PM: Goodnight
Session Descriptions
The Future of Open Table Formats & Unity Catalog
This talk explores the latest innovations in Delta Lake 4.0 and Apache Iceberg v3, focusing on how open-source Unity Catalog provides unified governance across modern open table formats. Participants will learn how advanced features like Liquid Clustering, Deletion Vectors.
From Internal Metrics to Customer-Facing Analytics: Building a Scalable Usage Insights Platform with Delta Sharing
For SaaS platforms, understanding how users interact with the product is essential for driving engagement, improving features, and delivering value. But what if those same usage metrics could also be shared back to the customers themselves, to help them understand their own adoption and push internal adoption across teams?
At DataGalaxy, we designed a scalable, self-service analytics experience that gives our customers real-time visibility into how their teams are using our platform. The twist? Instead of embedding dashboards powered by yet another internal data store, we expose a Delta Sharing feed, enriched and aggregated from our centralized usage pipeline, and connect it directly to embedded Superset dashboards.
By turning usage data into a shareable, secure product layer, we're redefining what embedded analytics can look like for SaaS — without compromising control, performance, or maintainability.
Beyond Data Ingestion, What Data Engineering Design Patterns for Open Lakehouse
Every lakehouse project starts with data ingestion. However, true operational maturity, scale, and maintainability are achieved only by moving beyond ingestion and adopting structured architectural solutions to complex post-ingestion problems.
In this session we'll be going through different data engineering design patterns you can apply to your workloads to make your engineering life easier and your consumers happier!