Disseminate

In this episode of the DuckDB in Research series, we’re joined by Arjen de Vries, Professor of Data Science at Radboud University. Arjen dives into his team’s development of a DuckDB extension for FAISS, a library originally developed at Facebook for efficient similarity search and vector operations.We explore the growing importance of embeddings and dense retrieval in modern information retrieval systems, and how DuckDB’s zero-copy architecture and tight integration with the Python ecosystem make it a compelling choice for managing large-scale vector data. Arjen shares insights into the technical challenges and architectural decisions behind the extension, comparisons with DuckDB’s native VSS (vector search) solution, and the broader vision of integrating vector search more deeply into relational databases.Along the way, we also touch on DuckDB's extension ecosystem, its potential for future research, and why tools like this are reshaping how we build and query modern AI-enabled systems.

In this episode, we sit down with David Justen to discuss his work on POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance which was implemented in DuckDB. David shares his journey in the database space, insights into performance optimization, and the challenges of working with modern analytical workloads. We dive into the intricacies of query compilation, vectorized execution, and how DuckDB is shaping the future of in-memory databases. Tune in for a deep dive into database internals, industry trends, and what’s next for high-performance data processing!Links: VLDB 2024 PaperDavid's Homepage

In this episode, we sit down with Daniël ten Wolde, a PhD researcher at CWI’s Database Architectures Group, to explore DuckPGQ—an extension to DuckDB that brings powerful graph querying capabilities to relational databases. Daniel shares his journey into database research, the motivations behind DuckPGQ, and how it simplifies working with graph data. We also dive into the technical challenges of implementing SQL Property Graph Queries (SQL PGQ) in DuckDB, discuss performance benchmarks, and explore the future of DuckPGQ in graph analytics and machine learning. Tune in to learn how this cutting-edge extension is bridging the gap between research and industry!Links:DuckPGQ homepageCommunity extensionDaniel's homepage

In this episode we kick off our DuckDB in Research series with Till Döhmen, a software engineer at MotherDuck, where he leads AI efforts. Till shares insights into DuckDQ, a Python library designed for efficient data quality validation in machine learning pipelines, leveraging DuckDB’s high-performance querying capabilities.We discuss the challenges of ensuring data integrity in ML workflows, the inefficiencies of existing solutions, and how DuckDQ provides a lightweight, drop-in replacement that seamlessly integrates with scikit-learn. Till also reflects on his research journey, the impact of DuckDB’s optimizations, and the future potential of data quality tooling. Plus, we explore how AI tools like ChatGPT are reshaping research and productivity. Tune in for a deep dive into the intersection of databases, machine learning, and data validation!Resources:GitHubPaperSlidesTill's Homepagedatasketches extension (released by a DuckDB community member 2 weeks after we recorded!)

Hey folks! We have been collaborating with everyone's favourite in-process SQL OLAP database management system DuckDB to bring you a new podcast series - the DuckDB in Research series!At Disseminate our mission is to bridge the gap between research and industry by exploring research that has a real-world impact. DuckDB embodies this synergy—decades of research underpin its design, and now it’s making waves in the research community as a platform for others to build on and this is what the series will focus on! Join us as we kick off the series with:📌 Daniel ten Wolde – DuckPGQ, a graph workload extension for DuckDB supporting SQL/PGQ📌 David Justen – POLAR: Adaptive, non-invasive join order selection 📌 Till Döhmen – DuckDQ: A Python library for data quality checks in ML pipelines📌 Arjen de Vries – FAISS extension for vector similarity search in DuckDB📌 Harry Gavriilidis – SheetReader: Efficient spreadsheet parsingWhether you're a researcher, engineer, or just curious about the intersection of databases and innovation we are sure you will love this series. Subscribe now and stay tuned for our first episode! 🚀

In this High Impact in Databases episode we talk to Anastasia Ailamaki.Anastasia is a Professor of Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL). Tune in to hear Anastasia's story! The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust.You can find Anastasia on:HomepageGoogle ScholarLinkedIn