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The 1.4 release of DuckDB supports using a DuckDB database to serve vector tiles! Of course, I had to try this out in R. Check it out: all 242,000 US Census block groups dynamically served as vector tiles from a DuckDB database, displayed on a MapLibre map from R...

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Kyle Walker

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45,518 просмотров • 10 месяцев назад •via X (Twitter)

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640,000 parcels in Dallas County Texas, visualized in 3D by appraised value per square foot. The data prep? Vector tiles generated in 15 seconds directly from a DuckDB database using my latest geospatial tool, {freestiler}. Package announcement coming tomorrow!
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640,000 parcels in Dallas County Texas, visualized in 3D by appraised value per square foot. The data prep? Vector tiles generated in 15 seconds directly from a DuckDB database using my latest geospatial tool, {freestiler}. Package announcement coming tomorrow!

Kyle Walker

46,418 просмотров • 5 месяцев назад

🚀 Visualize Big Vector Data in Python—FAST! Tired of slow and clunky tools for visualizing big vector dataset in Python? Discover this one-line Python trick using Leafmap + DuckDB to instantly render huge vector datasets—even gigabytes in size—right in your Jupyter notebook. In this video, you'll learn how to: ✅ Visualize millions of vector features with ease ✅ Use Leafmap + DuckDB to serve vector tiles dynamically ✅ Handle GeoParquet, GeoPackage, cloud-hosted data & more ✅ Create fully interactive maps—without crashing your browser! 📌 Watch the full video tutorial on YouTube: 👉 Notebook example: #Visualization #Leafmap #BigData #DuckDB #GeoPython #GIS #OpenSource
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🚀 Visualize Big Vector Data in Python—FAST! Tired of slow and clunky tools for visualizing big vector dataset in Python? Discover this one-line Python trick using Leafmap + DuckDB to instantly render huge vector datasets—even gigabytes in size—right in your Jupyter notebook. In this video, you'll learn how to: ✅ Visualize millions of vector features with ease ✅ Use Leafmap + DuckDB to serve vector tiles dynamically ✅ Handle GeoParquet, GeoPackage, cloud-hosted data & more ✅ Create fully interactive maps—without crashing your browser! 📌 Watch the full video tutorial on YouTube: 👉 Notebook example: #Visualization #Leafmap #BigData #DuckDB #GeoPython #GIS #OpenSource

Qiusheng Wu

13,147 просмотров • 10 месяцев назад

We're excited to announce duckdb-skills, a DuckDB plugin for Claude Code! We think the embedded nature of DuckDB makes it a perfect companion for Claude in your local workflows. The skills supported include: + read-file and query – uses DuckDB's CLI to query data locally, unlocking easy access to any file that DuckDB can read. + read-memories – a clever idea to store your Claude memories in DuckDB and query them at blazing speed. These are powered by two additional skills: + attach-db – gives Claude a mechanism to manage DuckDB state through a .sql file linked to your project. + duckdb-docs – uses a remote DuckDB full-text search database to query the DuckDB docs and answer all of your (and Claude's own) questions.
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We're excited to announce duckdb-skills, a DuckDB plugin for Claude Code! We think the embedded nature of DuckDB makes it a perfect companion for Claude in your local workflows. The skills supported include: + read-file and query – uses DuckDB's CLI to query data locally, unlocking easy access to any file that DuckDB can read. + read-memories – a clever idea to store your Claude memories in DuckDB and query them at blazing speed. These are powered by two additional skills: + attach-db – gives Claude a mechanism to manage DuckDB state through a .sql file linked to your project. + duckdb-docs – uses a remote DuckDB full-text search database to query the DuckDB docs and answer all of your (and Claude's own) questions.

DuckDB

60,694 просмотров • 5 месяцев назад

Built a spatial SQL explorer for my GIS students this week. Drop a GeoJSON, practice writing queries, see instant results on a map + linked table. All in-browser, nothing to install. Powered by DuckDB WASM + MapLibre. Try it out:
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Built a spatial SQL explorer for my GIS students this week. Drop a GeoJSON, practice writing queries, see instant results on a map + linked table. All in-browser, nothing to install. Powered by DuckDB WASM + MapLibre. Try it out:

Kyle Walker

21,088 просмотров • 6 месяцев назад

DuckDB just released Quack: a client/server protocol over HTTP. Any DuckDB can serve (quack_serve()), any DuckDB can attach to it. DuckDB-Wasm included since it's plain HTTP on port 9494. Some fun stuff : - A browser tab talking directly to a DuckDB server somewhere on the internet - Local-first apps with a remote target that speaks the exact same dialect - Notebook-to-notebook query forwarding Curious what people will build with this. We've got a few ideas brewing at MotherDuck too :) And while we're at it, can we finally retire the "DuckDB has no multi-writer support" take? There are plenty of options out there now, just depends how you want to slice and dice.
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DuckDB just released Quack: a client/server protocol over HTTP. Any DuckDB can serve (quack_serve()), any DuckDB can attach to it. DuckDB-Wasm included since it's plain HTTP on port 9494. Some fun stuff : - A browser tab talking directly to a DuckDB server somewhere on the internet - Local-first apps with a remote target that speaks the exact same dialect - Notebook-to-notebook query forwarding Curious what people will build with this. We've got a few ideas brewing at MotherDuck too :) And while we're at it, can we finally retire the "DuckDB has no multi-writer support" take? There are plenty of options out there now, just depends how you want to slice and dice.

Mehdi Ouazza

65,776 просмотров • 3 месяцев назад

GeoLibre v1.10.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings photorealistic city-scale 3D to the map, adds OGC API vector tiles, and makes vector tiles and live editing easier to work with. What's new in v1.10.0 - Google Photorealistic 3D Tiles: drape entire cities in photorealistic 3D right in the map, streamed and rendered through - ArcGIS I3S scene layers: stream Integrated Mesh and 3D Object scene layers and render them in 3D on the shared instance. - OGC API vector tiles: add standards-based OGC API - Tiles vector tile services as a remote source, right alongside XYZ and ArcGIS vector tiles. - Local HDF5 and NetCDF-4 loading: open scientific HDF5 and NetCDF-4 files straight from disk, no cloud references required. - GeoEditor from the current view: pull the vector features currently visible in the map into the GeoEditor and start editing what you are looking at, without re-importing the source data. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre
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GeoLibre v1.10.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings photorealistic city-scale 3D to the map, adds OGC API vector tiles, and makes vector tiles and live editing easier to work with. What's new in v1.10.0 - Google Photorealistic 3D Tiles: drape entire cities in photorealistic 3D right in the map, streamed and rendered through - ArcGIS I3S scene layers: stream Integrated Mesh and 3D Object scene layers and render them in 3D on the shared instance. - OGC API vector tiles: add standards-based OGC API - Tiles vector tile services as a remote source, right alongside XYZ and ArcGIS vector tiles. - Local HDF5 and NetCDF-4 loading: open scientific HDF5 and NetCDF-4 files straight from disk, no cloud references required. - GeoEditor from the current view: pull the vector features currently visible in the map into the GeoEditor and start editing what you are looking at, without re-importing the source data. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre

Qiusheng Wu

24,578 просмотров • 1 месяц назад

GeoLibre v0.9.0 is out. GeoLibre is a lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data across desktop and web environments, with a responsive layout for mobile screens. This release turns GeoLibre into a far more complete analysis environment. Highlights: - New cloud data panels for Microsoft Planetary Computer and Google Earth Engine, plus an Overture Maps plugin and federal Web Services plugins, including NASA Earthdata, USGS National Map, EPA EnviroAtlas, FEMA NFHL - Accessing 1000+ NASA Earthdata layers with a few simple clicks - Time Slider plugin for animating time series raster and vector data - SQL Workspace: run DuckDB Spatial SQL directly against loaded layers, local files, and remote URLs, with query history and one-click "add results to map" - Conversion tools: Vector to GeoParquet, CSV to GeoParquet, and Raster to COG, running in the browser with DuckDB-WASM - Controls menu with Measure, Bookmark, Minimap, View State, and Print Everything is open source. Try it in your browser, install the desktop app, or self-host it. Live demo: GitHub: Feedback and contributions are welcome. #GIS #Geospatial #OpenSource #MapLibre #DuckDB #WebGIS #GeoLibre
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GeoLibre v0.9.0 is out. GeoLibre is a lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data across desktop and web environments, with a responsive layout for mobile screens. This release turns GeoLibre into a far more complete analysis environment. Highlights: - New cloud data panels for Microsoft Planetary Computer and Google Earth Engine, plus an Overture Maps plugin and federal Web Services plugins, including NASA Earthdata, USGS National Map, EPA EnviroAtlas, FEMA NFHL - Accessing 1000+ NASA Earthdata layers with a few simple clicks - Time Slider plugin for animating time series raster and vector data - SQL Workspace: run DuckDB Spatial SQL directly against loaded layers, local files, and remote URLs, with query history and one-click "add results to map" - Conversion tools: Vector to GeoParquet, CSV to GeoParquet, and Raster to COG, running in the browser with DuckDB-WASM - Controls menu with Measure, Bookmark, Minimap, View State, and Print Everything is open source. Try it in your browser, install the desktop app, or self-host it. Live demo: GitHub: Feedback and contributions are welcome. #GIS #Geospatial #OpenSource #MapLibre #DuckDB #WebGIS #GeoLibre

Qiusheng Wu

42,457 просмотров • 2 месяцев назад

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)
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I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

Victor

261,537 просмотров • 1 год назад

Meet Database Agents. A team of li’l librarians in your database — keeping it up to date automatically using context from the page, your workspace, and the web. No more “we’ll update the database later.” Starting to roll out on Biz + Enterprise.
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Meet Database Agents. A team of li’l librarians in your database — keeping it up to date automatically using context from the page, your workspace, and the web. No more “we’ll update the database later.” Starting to roll out on Biz + Enterprise.

Notion

299,577 просмотров • 5 месяцев назад

I am very excited to release GeoLibre v1.0 🌍 GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre
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I am very excited to release GeoLibre v1.0 🌍 GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre

Qiusheng Wu

39,593 просмотров • 2 месяцев назад

Introducing the GeoLibre R package! GeoLibre already runs as a desktop app, a web app, and a Jupyter widget. Now it runs in R too. The new geolibre R package is an htmlwidgets interface that embeds the full GeoLibre GIS in the RStudio Viewer, Quarto, R Markdown, and Shiny. What you get: - A real GIS, not a static map. Layer panel, styling, drawing tools, identify, and the processing toolbox are all there inside your R session. - Bring your own data. `sf` objects, GeoJSON, CSV and XY tables, Shapefile, GeoPackage, FlatGeobuf, GeoParquet, KML, Cloud Optimized GeoTIFF, XYZ, WMS, WMTS, WFS, PMTiles, vector tiles, 3D Tiles, and even video overlays. - Shiny, in both directions. `geolibre_proxy()` updates a live map without rebuilding it, and drawn features, selected features, identify results, and the current view come back as Shiny inputs. You can even run processing algorithms from the server. The package is on GitHub now while the first CRAN submission is prepared. Try it out - R package website: - Live interactive example: - Source: - GeoLibre Web: - Documentation: #RStats #GIS #Geospatial #OpenSource #Shiny #MapLibre #GeoLibre
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Introducing the GeoLibre R package! GeoLibre already runs as a desktop app, a web app, and a Jupyter widget. Now it runs in R too. The new geolibre R package is an htmlwidgets interface that embeds the full GeoLibre GIS in the RStudio Viewer, Quarto, R Markdown, and Shiny. What you get: - A real GIS, not a static map. Layer panel, styling, drawing tools, identify, and the processing toolbox are all there inside your R session. - Bring your own data. `sf` objects, GeoJSON, CSV and XY tables, Shapefile, GeoPackage, FlatGeobuf, GeoParquet, KML, Cloud Optimized GeoTIFF, XYZ, WMS, WMTS, WFS, PMTiles, vector tiles, 3D Tiles, and even video overlays. - Shiny, in both directions. `geolibre_proxy()` updates a live map without rebuilding it, and drawn features, selected features, identify results, and the current view come back as Shiny inputs. You can even run processing algorithms from the server. The package is on GitHub now while the first CRAN submission is prepared. Try it out - R package website: - Live interactive example: - Source: - GeoLibre Web: - Documentation: #RStats #GIS #Geospatial #OpenSource #Shiny #MapLibre #GeoLibre

Qiusheng Wu

24,670 просмотров • 20 дней назад

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:
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New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

Andrew Ng

137,091 просмотров • 2 лет назад

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.
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Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Harnoor Singh

13,348 просмотров • 5 месяцев назад

Simplify database management using Declarative Schema! With declarative schema, you can have a single source of truth for the shape of your database in a single file! No more having to go through multiple migration files to figure out the schema of your database!
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Simplify database management using Declarative Schema! With declarative schema, you can have a single source of truth for the shape of your database in a single file! No more having to go through multiple migration files to figure out the schema of your database!

Tyler Shukert

14,999 просмотров • 1 год назад

GeoLibre v1.1 is out! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release builds on the v1.0 foundation with features that make day-to-day mapping smoother: - Attribute table management — rename, delete, hide/show, and reorder fields right in the table, with the layout saved to your project. - Data-driven symbology for Add Vector Layer — single, categorized, graduated, and expression-based styling now applies to maplibre-gl-vector layers. - Atmosphere Effects plugin — a deep-space backdrop, parallax starfield, comets, and a glowing globe halo at low zoom (toggle it from the Controls menu). - Layer panel upgrades — rename layers, open the attribute table, and export straight from the layer actions menu. - Auto Refresh extended to Add Vector Layer URL layers. - In-browser GeoPandas via Pyodide — run the vector tools with no Python sidecar, same results. - Plugin API — external plugins can now render on the host's shared instance via app.getDeckGL(). Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre
0:55

GeoLibre v1.1 is out! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release builds on the v1.0 foundation with features that make day-to-day mapping smoother: - Attribute table management — rename, delete, hide/show, and reorder fields right in the table, with the layout saved to your project. - Data-driven symbology for Add Vector Layer — single, categorized, graduated, and expression-based styling now applies to maplibre-gl-vector layers. - Atmosphere Effects plugin — a deep-space backdrop, parallax starfield, comets, and a glowing globe halo at low zoom (toggle it from the Controls menu). - Layer panel upgrades — rename layers, open the attribute table, and export straight from the layer actions menu. - Auto Refresh extended to Add Vector Layer URL layers. - In-browser GeoPandas via Pyodide — run the vector tools with no Python sidecar, same results. - Plugin API — external plugins can now render on the host's shared instance via app.getDeckGL(). Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre

Qiusheng Wu

20,953 просмотров • 2 месяцев назад

everybody talks about building AI chatbots, but nobody tells you HOW to do it that's why I made a full practical walkthrough on how to build an AI chatbot that's hooked up to your own custom knowledgebase inside of the walk-through i go over: – data collection: gathering all relevant documents, conversations, and info - preprocessing: cleaning up and formatting the collected data - chunking: break down the cleaned data into smaller, manageable pieces - embedding & storing in a vector database - RAG & chatbot integration: using RAG to allow the chatbot to retrieve relevant information from the vector database based on a user's question reply to this tweet w/ the word “RAG” & I’ll send it to you (must be following so I can DM)
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everybody talks about building AI chatbots, but nobody tells you HOW to do it that's why I made a full practical walkthrough on how to build an AI chatbot that's hooked up to your own custom knowledgebase inside of the walk-through i go over: – data collection: gathering all relevant documents, conversations, and info - preprocessing: cleaning up and formatting the collected data - chunking: break down the cleaned data into smaller, manageable pieces - embedding & storing in a vector database - RAG & chatbot integration: using RAG to allow the chatbot to retrieve relevant information from the vector database based on a user's question reply to this tweet w/ the word “RAG” & I’ll send it to you (must be following so I can DM)

Tyler

83,577 просмотров • 1 год назад

Let Postgres own the Iceberg catalog and delegate analytics to DuckDB. The result => transactional lakehouse updates with fast analytical queries. This isn’t a concept. It’s exactly what pg_lake delivers today. pg_lake combines a set of extensions and components that let you query and modify Iceberg tables (and other lakehouse formats) directly from Postgres. DuckDB is used to accelerate analytical queries and runs in a sidecar process called pgduck_server, which communicates with Postgres during query execution. How it works (diagram below): 1. An application sends a query to Postgres to calculate unrealized PnL (Profit and Loss) for the Disney ticker. 2. Postgres parses the query and identifies the part that computes the average price from historical lakehouse data. 3. That part is forwarded to pgduck_server for accelerated execution. 4. pgduck_server delegates execution to DuckDB, which queries the lakehouse (reusing cached data if available). 5. DuckDB computes the average price and returns it to Postgres. 6. Postgres joins the result with local portfolio data and computes the unrealized PnL. 7. The final result is returned to the application.
0:24

Let Postgres own the Iceberg catalog and delegate analytics to DuckDB. The result => transactional lakehouse updates with fast analytical queries. This isn’t a concept. It’s exactly what pg_lake delivers today. pg_lake combines a set of extensions and components that let you query and modify Iceberg tables (and other lakehouse formats) directly from Postgres. DuckDB is used to accelerate analytical queries and runs in a sidecar process called pgduck_server, which communicates with Postgres during query execution. How it works (diagram below): 1. An application sends a query to Postgres to calculate unrealized PnL (Profit and Loss) for the Disney ticker. 2. Postgres parses the query and identifies the part that computes the average price from historical lakehouse data. 3. That part is forwarded to pgduck_server for accelerated execution. 4. pgduck_server delegates execution to DuckDB, which queries the lakehouse (reusing cached data if available). 5. DuckDB computes the average price and returns it to Postgres. 6. Postgres joins the result with local portfolio data and computes the unrealized PnL. 7. The final result is returned to the application.

Denis Magda

28,503 просмотров • 6 месяцев назад

A minute of chaos in the simulator when a thrust vector malfunction sent us out of control during the perigee raise maneuver. The system handled it as expected, shutdown the engine, and allowed us to safely return to Earth.
1:13

A minute of chaos in the simulator when a thrust vector malfunction sent us out of control during the perigee raise maneuver. The system handled it as expected, shutdown the engine, and allowed us to safely return to Earth.

Reid Wiseman

274,607 просмотров • 1 год назад