# DuckDB DuckDB is an open-source analytical SQL database system that runs in-process and can be deployed from edge devices to servers. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## DuckDB Skills for Claude Code DevFeed: [DuckDB Skills for Claude Code]() Original publisher: [Read original article]() Author: The DuckDB team Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [DuckDB]() Topics: [DuckDB](), [Claude Code](), [Command-line interface](), [data](), [CSV](), [parquet]() Tags: [claude-code](), [cli](), [csv](), [data](), [duckdb](), [parquet](), [using-duckdb]() ### AI overview This post introduces the duckdb-skills plugin for Claude Code. The plugin uses the DuckDB CLI to inspect data files, run SQL queries, convert formats, explore object storage and spatial data, search documentation, and recall earlier sessions. It explains installation, how the skills select appropriate commands, and how Claude uses DuckDB for exact query results. ### Source excerpt The duckdb-skills plugin gives Claude Code a growing number of skills that use the DuckDB CLI to read data files, run queries, convert formats, explore object storage, work with spatial data, search the documentation and recall earlier sessions. ## DuckDB and the changing physics of analytics DevFeed: [DuckDB and the changing physics of analytics]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-08-26T14:00:00Z Content type: opinion Language: en Sources: [All Things Distributed]() Topics: [DuckDB](), [data](), [Databases](), [Amazon S3](), [Amazon Web Services](), [Computer science]() Tags: [analytics](), [aws](), [data](), [databases](), [duckdb](), [posts](), [s3](), [software](), [systems]() ### AI overview The article describes how changing relative costs for compute, memory, and networking are enabling more data processing to remain within applications. It presents DuckDB as a database supporting this shift and discusses its relationship to AWS data offerings, including S3 Files, S3 Tables, and S3 Vectors, alongside DuckLabs joining AWS. ### Source excerpt In this post, Andy Warfield explains how databases like DuckDB are enabling a new way to build with data, why they matter right now, and how they complement the work we've been doing in S3 (e.g., S3 Files, S3 Tables, S3 Vectors). And most importantly, why DuckLabs, the team behind DuckDB, is joining AWS ## DuckLabs to Join AWS, Projects to Remain Open Source DevFeed: [DuckLabs to Join AWS, Projects to Remain Open Source]() Original publisher: [Read original article]() Author: Mark Raasveldt and Hannes Mühleisen Published: 2026-08-26T00:00:00Z Content type: news Language: en Sources: [DuckDB]() Topics: [DuckDB](), [Open Source](), [Amazon Web Services (AWS)](), [amazon]() Tags: [amazon](), [amazon-web-services-aws](), [deep-dive](), [duckdb](), [open-source](), [projects](), [software]() ### AI overview DuckLabs will join Amazon Web Services as a new subsidiary, with the change expected to take effect in early September. DuckDB, DuckLake, Quack, and related extensions will remain free and open source under the MIT license, with their roadmap, governance model, and development pace unchanged. ### Source excerpt DuckLabs will join Amazon Web Services (AWS), which is expected to be effective in early September. The projects will remain open-source under the MIT license. ## Agent Night demo recap: Brian Douglas's self-healing Pokémon agent DevFeed: [Agent Night demo recap: Brian Douglas's self-healing Pokémon agent]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T18:34:12Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Demo](), [observability ai agents](), [telemetry](), [Claude Code](), [Python](), [DuckDB](), [Kafka](), [Code](), [Open Source]() Tags: [agent](), [claude-code](), [code](), [coding](), [demo](), [duckdb](), [kafka](), [open-source](), [python](), [self-healing](), [telemetry](), [traces]() ### AI overview A recap of Brian Douglas's Agent Night demo shows how Paper Compute ran Pokémon as a self-healing agent for six months. The setup uses Claude Code, a headless Python emulator, and traces from each session and turn. The article explains how labeled traces and per-session reflections can make the agent's history searchable and useful for improvement. ### Source excerpt Brian Douglas of Paper Compute closed Agent Night with six months of running Pokémon as a self-healing agent: traces, anomalies, dreams, and inceptions. ## Data Engineering Weekly #280 DevFeed: [Data Engineering Weekly #280]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-27T03:37:19Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Data Infrastructure](), [Open Source](), [Data Quality](), [DuckDB](), [Database]() Tags: [data](), [data-engineering](), [data-infrastructure](), [data-quality](), [database](), [duckdb](), [engineering](), [open-source](), [python]() ### AI overview Data Engineering Weekly #280 is a newsletter roundup covering updates to leetdata.ai and aidataengineer.io, agent-oriented data infrastructure, open-source modern data stack tools, data-tool landscapes, metric certification, and data quality in the AI era. ### Source excerpt The Weekly Data Engineering Newsletter ## The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering DevFeed: [The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-08T12:16:09Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [pandas](), [data-engineering](), [Artificial Intelligence](), [DuckDB](), [data-platforms](), [software-development](), [future of software](), [Apache Spark](), [Hadoop](), [Open Source](), [parquet]() Tags: [ai](), [apache-arrow](), [arrow](), [data](), [data-engineering](), [data-platforms](), [duckdb](), [hadoop](), [large-language-models](), [open-source](), [pandas](), [parquet](), [software-development]() ### AI overview An interview with Wes McKinney covers the origins of pandas and Apache Arrow, the evolution of modern data engineering from Hadoop to lakehouse architectures, and the roles of tools such as Parquet, DuckDB, DataFusion, and Spark. McKinney also discusses how AI affects software development, arguing that it can improve experienced engineers' productivity but does not replace software engineering, architecture, or judgment. ### Source excerpt interview with Wes McKinney ## RAG in Go: A Vulnerability Research Tool DevFeed: [RAG in Go: A Vulnerability Research Tool]() Original publisher: [Read original article]() Published: 2026-04-20T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Retrieval Augmented Generation (RAG)](), [Go Language](), [vulnerability](), [DuckDB](), [JSON]() Tags: [api](), [ardan-labs](), [blog](), [code](), [database](), [duckdb](), [embedding](), [go](), [go-programming](), [golang](), [json](), [llm](), [programming](), [rag](), [retrieval](), [technical](), [vulnerability]() ### AI overview This tutorial demonstrates how to build a retrieval-augmented generation tool in Go using the Go Vulnerability Database as internal documents. It covers ingesting zipped JSON data, generating vector embeddings, storing them in DuckDB, and retrieving documents relevant to a user query. ### Source excerpt Introduction In the previous post, you saw how you can use tools to add information to an LLM query. In this post, we'll see another method of adding information to an LLM called RAG, or Retrieval-Augmented Generation. The idea of RAG is that you want the LLM to have access to information that wasn't available to it when it was initially trained. You do it by storing documents in your own database along with their embedding. I won't go into the technical details of embedding, but think of it as a way to convert a piece of text into a vector. The magic is that if two pieces of text have similar meaning, an embedding model can create vectors that mathematically show they are similar. ## Data Tables & Ducklake: managed storage for workflows DevFeed: [Data Tables & Ducklake: managed storage for workflows]() Original publisher: [Read original article]() Author: Diego Imbert Published: 2026-03-31T00:00:00Z Content type: release Language: en Sources: [Windmill Blog]() Topics: [data](), [Databases](), [Amazon S3](), [PostgreSQL](), [DuckDB](), [Orchestration]() Tags: [data](), [databases](), [duckdb](), [launch-week](), [orchestration](), [postgresql](), [s3](), [sql](), [storage]() ### AI overview Windmill introduces Data Tables, a workspace-scoped PostgreSQL layer with managed credentials and SQL access, and Ducklake, a data lakehouse backed by S3. The release is designed to let workflows read and write relational and analytical data within the Windmill platform. ### Source excerpt Introducing Data Tables and Ducklake: managed SQL storage and S3-backed data lakehouse in Windmill. ## OpenDroneLog: A Self Hosted DJI Flight Log Dashboard DevFeed: [OpenDroneLog: A Self Hosted DJI Flight Log Dashboard]() Original publisher: [Read original article]() Author: Jeremy Published: 2026-03-02T14:34:38Z Content type: article Language: en Sources: [Noted]() Topics: [Flight](), [dashboards](), [Self-hosted](), [Local-First](), [Docker](), [DuckDB](), [React](), [Tauri](), [telemetry]() Tags: [docker](), [duckdb](), [local](), [local-first](), [maps](), [react](), [self-hosted](), [self-hosted-logging](), [storage](), [tauri](), [telemetry]() ### AI overview OpenDroneLog is a self-hosted dashboard for analyzing DJI flight logs and Litchi CSV exports. It organizes local telemetry into maps, charts, battery-health tracking, maintenance data, and printable reports, with desktop and Docker-deployable web versions. ### Source excerpt OpenDroneLog is a self hosted flight log dashboard for DJI and Litchi files that turns raw telemetry into maps, charts, battery health insights, and printable reports using a Docker deployable web app with local first storage. ## Query Database Using Plain English DevFeed: [Query Database Using Plain English]() Original publisher: [Read original article]() Published: 2026-02-11T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Databases](), [Large Language Model](), [SQL](), [Code](), [DuckDB](), [Ollama](), [context](), [GitHub](), [API]() Tags: [api](), [ardan-labs](), [blog](), [code](), [context](), [database](), [duckdb](), [github](), [go](), [go-programming](), [golang](), [llms](), [ollama](), [programming](), [sql]() ### AI overview This tutorial shows how to build a system that lets users query a relational database using plain English. It uses an LLM to generate SQL, executes the query against a database, and then uses the query results to generate an answer. The example uses the Austin Bike Share dataset, Kronk Model Server, and DuckDB, while noting that other model servers and SQL databases can be used. ### Source excerpt Introduction In this post you'll see how you can create a system that allows users to query a relational database using plain English. This allows users not familiar with SQL or business intelligence systems to get insights from data. Setting Up If you want to follow along, you'll need to clone the code from the GitHub repo. This will download the code, and the database file containing the data (bikes.ddb) Note: The data is from the Austin Bike Share dataset. ## Processing 1 TB with DuckDB in less than 30 seconds DevFeed: [Processing 1 TB with DuckDB in less than 30 seconds]() Original publisher: [Read original article]() Author: Matt Martin Published: 2025-12-23T19:58:37Z Content type: article Language: en Sources: [DataExpert.io Newsletter]() Topics: [DuckDB](), [Benchmark](), [data](), [parquet](), [Python](), [Processes]() Tags: [benchmark](), [data](), [duckdb](), [parquet](), [process](), [python]() ### AI overview This article benchmarks DuckDB on progressively larger datasets, reporting that it read approximately 200 GB in under 10 seconds and 500 GB in about 40 seconds. It also describes generating a 1 TB dataset in roughly 70 minutes on an M2 Pro Mac with 16 GB of RAM using 10 parallel workers. ### Source excerpt And so can you ## Comparing Integers and Doubles DevFeed: [Comparing Integers and Doubles]() Original publisher: [Read original article]() Author: Thomas Neumann (noreply@blogger.com) Published: 2025-11-10T16:55:00Z Content type: article Language: en Sources: [Database Architects]() Topics: [floating-point](), [SQL](), [DuckDB](), [sql-server](), [Programming](), [C++]() Tags: [c-plus-plus](), [compiler](), [duckdb](), [floating-point](), [precision](), [sql](), [sql-server](), [testing](), [undefined-behavior]() ### AI overview The article explains how comparing large integers with double-precision values can lose integer precision and produce non-transitive results in SQL systems. It describes how this can cause differences between ordinary comparisons and hash joins, and outlines a conversion-based approach for correct comparisons. ### Source excerpt During automated testing we stumbled upon a problem that boiled down to transitive comparisons: If a=b, and a=c, when we assumed that b=c. Unfortunately that is not always the case, at least not in all systems. Consider the following SQL query: select a=b, a=c, b=c from (values( 1234567890123456789.0::double precision, 1234567890123456788::bigint, 1234567890123456789::bigint)) s(a,b,c) If you execute that in Postgres (or DuckDB, or SQL Server, or ...) the answer is (true, true, false). That is, the comparison is not transitive! Why does that happen? When these systems compare a bigint and a double, they promote the bigint to double and then compare. But a double has only 52 bits of mantissa, which means it will lose precision when promoting large integers to double, producing false positives in the comparison. This behavior is highly undesirable, first because it confuses the optimizer, and second because (at least in our system) joins work very differently: Hash joins promote to the most restrictive type and discard all values that cannot be represented, as they will never produce a join partner for sure. For double/bigint joins that leads to observable differences between joins and plain comparisons, which is very bad. How should we compare correctly? Conceptually the situation is clear, an IEEE 754 floating point with sign s, mantissa m, and exponent e represents the values (-1)^s*m*2^e, we just have to compare the integer with that value. But there is no easy way to do that, if we do a int/double comparison in, e.g., C++, the compiler does the same promotion to double, messing up the comparison. We can get the logic right by doing two conversions: We first convert the int to double and compare that. If the values are not equal, the order is clear and we can use that. Otherwise, we convert the double back to an integer and check if the conversion rounded up or down, and handle the result. Plus some extra checks to avoid undefined behavior (the conversion of intma ## 3 demos to get started with Redpanda on Apache Iceberg™ DevFeed: [3 demos to get started with Redpanda on Apache Iceberg™]() Original publisher: [Read original article]() Author: Paul Wilkinson Published: 2025-10-07T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [Apache Iceberg integration](), [Kafka](), [Kubernetes](), [DuckDB](), [Streaming](), [Development]() Tags: [apache-iceberg](), [apache-iceberg-integration](), [config](), [development](), [duckdb](), [integration](), [kafka](), [kubernetes](), [no-code]() ### AI overview This article presents three demos for developing with Redpanda's Iceberg Topics in a local Kubernetes environment. The project demonstrates streaming data from Redpanda topics into Apache Iceberg tables without external cloud services or ETL, using Polaris, MinIO, and DuckDB for the end-to-end flow. ### Source excerpt Apache Iceberg integration made simple. Get started with three no-fluff demos using Redpanda's Iceberg Topics. One config. Endless scale. ## DuckDB benchmarked against Spark DevFeed: [DuckDB benchmarked against Spark]() Original publisher: [Read original article]() Author: Matt Martin Published: 2025-09-22T20:13:34Z Content type: comparison Language: en Sources: [DataExpert.io Newsletter]() Topics: [DuckDB](), [Apache Spark]() Tags: [duckdb](), [spark]() ### AI overview A comparison article about benchmarking DuckDB against Apache Spark. ### Source excerpt You Don't Always Need A Sledgehammer ## When SQL Meets Lambda Expressions DevFeed: [When SQL Meets Lambda Expressions]() Original publisher: [Read original article]() Author: lukaseder Published: 2025-03-27T13:04:44Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [SQL](), [Structured-data](), [DuckDB](), [clickhouse](), [databricks](), [PostgreSQL]() Tags: [apply](), [array](), [array-types](), [arrays](), [clickhouse](), [concatenation](), [databricks](), [duckdb](), [filter](), [function](), [java](), [jooq](), [kotlin](), [lambda](), [postgresql](), [scala](), [snowflake](), [sql](), [trino]() ### AI overview This article explains how SQL ARRAY types and lambda expressions are supported across several modern SQL dialects. It shows how jOOQ maps Java, Kotlin, and Scala lambda expressions to SQL expressions, including filtering arrays, and describes emulation with subqueries for dialects without lambda syntax. ### Source excerpt ARRAY types are a part of the ISO/IEC 9075 SQL standard. The standard specifies how to: But it is very unopinionated when it comes to function support. The ISO/IEC 9075-2:2023(E) 6.47 specifies concatenation of arrays, whereas the 6.48 section lists a not extremely useful TRIM_ARRAY function, exclusively (using which ... Continue reading When SQL Meets Lambda Expressions -> ## jOOQ 3.20 released with ClickHouse, Databricks, and much more DuckDB support, new modules, Oracle type hierarchies, more spatial support, decfloat and synonym support, hidden columns, Scala 3, Kotlin 2, and much more DevFeed: [jOOQ 3.20 released with ClickHouse, Databricks, and much more DuckDB support, new modules, Oracle type hierarchies, more spatial support, decfloat and synonym support, hidden columns, Scala 3, Kotlin 2, and much more]() Original publisher: [Read original article]() Author: lukaseder Published: 2025-02-20T10:27:54Z Content type: release Language: en Sources: [jOOQ]() Topics: [clickhouse](), [databricks](), [SQL](), [DuckDB](), [modules](), [Oracle Database](), [Kotlin](), [Scala](), [reactive](), [jpa]() Tags: [array](), [clickhouse](), [databricks](), [decfloat](), [dirty-tracking](), [dml-join](), [duckdb](), [hidden-columns](), [jdk-21](), [jooq](), [jooq-3-20](), [jooq-development](), [jpa](), [json](), [kotlin](), [kotlin-2](), [merge](), [modules](), [oracle](), [oracle-plsql-types](), [r2dbc](), [reactive](), [scala](), [scala-3](), [sequences](), [spatial](), [sql]() ### AI overview jOOQ 3.20 introduces experimental ClickHouse and Databricks SQL dialects, improves DuckDB support, adds new integration modules, and expands support for Oracle type hierarchies, spatial features, decfloat, synonyms, hidden columns, Scala 3, and Kotlin 2. ### Source excerpt New dialects: jOOQ 3.20 ships with 2 new experimental dialects: ClickHouse is a fast-moving SQL dialect with a historic vendor-specific syntax that is gradually migrated to a more standards compliant alternative, which is why our support is still experimental. A lot of behaviours differ from what one would expect elsewhere, including NULL handling, which is ... Continue reading jOOQ 3.20 released with ClickHouse, Databricks, and much more DuckDB support, new modules, Oracle type hierarchies, more spatial support, decfloat and synonym support, hidden columns, Scala 3, Kotlin 2, and much more -> ## Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B DevFeed: [Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B]() Original publisher: [Read original article]() Author: Andrea Soria; Till Döhmen; Sen Wu; Laurel Orr; Vishal Published: 2024-04-04T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [text2sql](), [DuckDB](), [hugging face](), [Large Language Model](), [SQL](), [API](), [parquet](), [data](), [Data analysis](), [Databases]() Tags: [ai](), [api](), [artificial-intelligence](), [datasets](), [duckdb](), [guide](), [hugging-face](), [llm](), [parquet](), [sql](), [text2sql]() ### AI overview This tutorial explains how to use the DuckDB-NSQL-7B large language model with the Hugging Face Dataset Viewer API, parquet files, and DuckDB to perform text2sql tasks. The model translates plain-language data requests into valid DuckDB SQL statements for data exploration and analysis. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## jOOQ 3.19.0 Released with DuckDB, Trino, Oracle 23c support, join path improvements, an official gradle plugin, commercial maven repositories, policies, UDT paths, trigger meta data, hierarchies, and much more DevFeed: [jOOQ 3.19.0 Released with DuckDB, Trino, Oracle 23c support, join path improvements, an official gradle plugin, commercial maven repositories, policies, UDT paths, trigger meta data, hierarchies, and much more]() Original publisher: [Read original article]() Author: lukaseder Published: 2023-12-15T16:30:41Z Content type: release Language: en Sources: [jOOQ]() Topics: [releases](), [DuckDB](), [CockroachDB](), [Maven](), [Gradle](), [rdbms](), [Multi-tenancy]() Tags: [cockroachdb](), [cockroachdb-23](), [duckdb](), [gradle](), [gradle-plugin](), [implicit-joins](), [java-8](), [join-paths](), [jooq](), [jooq-3-19](), [jooq-development](), [maven](), [maven-repository](), [multi-tenancy](), [oracle-23c](), [policies](), [release-notes](), [releases](), [row-level-security](), [security](), [trigger-meta-data](), [triggers](), [trino](), [trinodb](), [udt-paths](), [udts]() ### AI overview The jOOQ 3.19.0 release adds experimental DuckDB support, Trino support, and support for CockroachDB 23 and Oracle 23c. It also improves join paths, introduces an official Gradle plugin and a commercial Maven repository, and adds policies for row-level security. ### Source excerpt New Dialects It's been a few releases since we've added support for new dialects, but finally some very interesting RDBMS of increasing popularity have joined the jOOQ family including: These dialects are available in all jOOQ editions. New dialect versions In addition to these entirely new dialects, big new CockroachDB and Oracle versions have shipped: ... Continue reading jOOQ 3.19.0 Released with DuckDB, Trino, Oracle 23c support, join path improvements, an official gradle plugin, commercial maven repositories, policies, UDT paths, trigger meta data, hierarchies, and much more -> ## Music Stats with DuckDB DevFeed: [Music Stats with DuckDB]() Original publisher: [Read original article]() Author: Artur Dryomov Published: 2023-06-05T00:00:00Z Content type: article Language: en Sources: [Artur Dryomov]() Topics: [DuckDB](), [data](), [import](), [CSV](), [JSON]() Tags: [csv](), [data](), [duckdb](), [import](), [json](), [music]() ### AI overview The article describes using DuckDB to combine and analyze music scrobbles from sources including Last.fm and Spotify, working with JSON and CSV data in different formats. ### Source excerpt Music! Everyone likes music. Well, I certainly hope so. Stats! Well, not everyone likes stats but I certainly do. In this article I'll describe an approach to work with music scrobbles from different sources and in different formats using DuckDB. DuckDB is a perfect match for such ad-hoc scenarios -- it has a minimal footprint, great performance and useful features. Scrobbles I've been using multiple music sources over the past decade. Fortunately, I had a Last.fm account from the beginning. It allowed me to track almost everything I've listened -- resulting in almost 110+ thousands of scrobbles. Unfortunately, I haven't used it all the time. ## Databases in 2021: A Year in Review DevFeed: [Databases in 2021: A Year in Review]() Original publisher: [Read original article]() Author: Andy Pavlo Published: 2021-12-28T07:18:19Z Content type: opinion Language: en Sources: [Andy Pavlo - Carnegie Mellon University]() Topics: [Databases](), [PostgreSQL](), [MySQL](), [DuckDB](), [Benchmark](), [Open Source]() Tags: [amazon](), [amazon-aurora](), [benchmark](), [blog](), [databases](), [duckdb](), [elasticsearch](), [google](), [mongodb](), [mysql](), [performance](), [postgresql](), [year-in-review]() ### AI overview A retrospective on 2021 database-industry developments, focusing on PostgreSQL's growing adoption, compatibility across database systems, rankings, community discussion, funding, acquisitions, and industry setbacks. ### Source excerpt Andy's take on 2021 database industry happenings - PostgreSQL, Performance Wars, Passings, and Larry Ellison.