Beyond Market Intelligence/DuckDB

DuckDB

DuckDB on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on duckdb in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around duckdb, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Machine Learning

Pandas API for DuckDB, PostgreSQL & ClickHouse — keeping computation inside the database[P]

Introducing memFrame, an open-source DataFrame API designed to transform your data workflow. Instead of importing data into Python, memFrame compiles operations directly to SQL, enabling computation within databases like DuckDB, PostgreSQL, and ClickHouse. This approach empowers users to leverage the power of their databases for data inspection, cleaning, statistics, and more—all while minimizing data transfer. We’re releasing features incrementally, prioritizing stability and user feedback. Explore this innovative architecture, including its built-in multiagent capabilities for natural language interaction with your data.

Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
Towards Data Science

Running SQL Concurrently Across Three Remote DuckDB Servers with Quack

Explore a novel approach to data processing with "Running SQL Concurrently Across Three Remote DuckDB Servers with Quack." This experiment demonstrates a practical application of remote SQL execution, empowering users to leverage distributed resources for enhanced performance. Discover how Quack facilitates this process, offering a streamlined solution for complex queries. For those interested in building applications that accumulate understanding, consider "Designing a Persistent Knowledge Layer That Refuses to Guess," which details a vendor-neutral blueprint for RAG systems.

City2Graph: A Python library for Heterogeneous Graph Neural Networks and spatial analysis in urban systems [R]
Machine Learning

City2Graph: A Python library for Heterogeneous Graph Neural Networks and spatial analysis in urban systems [R]

Introducing City2Graph, a novel Python library designed to transform urban geospatial data into analysis-ready heterogeneous graphs. Recently published in *Computers, Environment and Urban Systems*, City2Graph streamlines workflows for spatial analysis and Graph Neural Networks, converting data from OpenStreetMap, Overture Maps, and GTFS feeds. It facilitates morphological, transport, mobility, and proximity analyses, offering seamless conversion between GeoDataFrames, NetworkX, and PyTorch Geometric.

The Medallion Data Architecture: An Introduction
Towards Data Science

The Medallion Data Architecture: An Introduction

Navigating modern data pipelines can feel complex, but the Medallion Data Architecture offers a clear, practical framework. This guide introduces the Bronze, Silver, and Gold layers—a proven approach to structuring data for reliability and analytical readiness. We’ll explore each tier with a working Python and DuckDB example, empowering you to build robust data workflows. For a deeper dive into related challenges in AI agent memory management, see "Asana's AI agents share memory across your company — but not your secrets."