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We are proud to release DuckDB v1.4.0, named “Andium” after the Andean teal (Anas andium), which lives in the Andean highlands of Colombia, Venezuela and Ecuador. In this blog post, we cover the most important updates for this release around support, features and extensions. DuckDB is moving rather quickly, and we could cover only a small fraction of the changes in this release. For the complete r
Introduction Data preprocessing is a necessary step in any machine learning workflow, affecting both the model’s effectiveness and the ease of maintenance. While scikit-learn is commonly used for preprocessing due to its integration with the broader Python ecosystem, DuckDB offers a practical alternative by enabling SQL-based data transformations within Python. Its declarative syntax supports modu
The first part of the blog post is shared with the DuckLake manifesto. Jump to the DuckLake extension section to read the rest. Background Innovative data systems like BigQuery and Snowflake have shown that disconnecting storage and compute is a great idea in a time where storage is a virtualized commodity. That way, both storage and compute can scale independently and we don't have to buy expensi
To install the new version, please visit the installation guide. Note that it can take a few hours to days to release some client libraries (e.g., Go, R, Java) and extensions (e.g., the UI) due to the extra changes and review rounds required. We are proud to release DuckDB 1.3.0. This release of DuckDB is named “Ossivalis” after Bucephala Ossivalis, an ancestor of the Goldeneye duck that lived mil
TL;DR: We are happy to announce a new preview feature that adds support for Apache Iceberg REST Catalogs, enabling DuckDB users to connect to Amazon S3 Tables and Amazon SageMaker Lakehouse with ease. The AWS Storage Blog also published a post on this feature, see Streamlining access to tabular datasets stored in Amazon S3 Tables with DuckDB. Iceberg Ahead! In recent years, the Iceberg open table
TL;DR: The DuckDB team and MotherDuck are excited to announce the release of a local UI for DuckDB shipped as part of the ui extension. The DuckDB project was built to make it simple to leverage modern database technology. DuckDB can be used from many popular languages and runs on a wide variety of platforms. The included Command Line Interface (CLI) provides a convenient way to interactively run
TL;DR: Securely read from and write to Google Sheets directly in DuckDB using the GSheets community extension! For ad hoc querying, authentication is as easy as logging into Google from a browser. Scheduled workflows can use persistent DuckDB Secrets. SQL-on-Sheets has arrived! Spreadsheets Are Everywhere Is anything more polarizing for data folks than spreadsheets? Wait, don't answer that, we don
The Apache® Parquet™ Format Apache Parquet is a popular, free, open-source, column-oriented data storage format. Whereas database systems typically load data from formats such as CSV and JSON into database tables before analyzing them, Parquet is designed to be efficiently queried directly. Parquet considers that users often only want to read some of the data, not all of it. To accommodate this, P
CSV Files: Dethroning Parquet as the Ultimate Storage File Format — or Not? TL;DR: Data analytics primarily uses two types of storage format files: human-readable text files like CSV and performance-driven binary files like Parquet. This blog post compares these two formats in an ultimate showdown of performance and flexibility, where there can be only one winner. File Formats CSV Files Data is mo
Installation Documentation Getting Started Connect Data Import JSON Files Multiple Files Parquet Files Partitioning Appender INSERT Statements Client APIs Overview ADBC C C++ CLI Dart Go Java (JDBC) Julia Node.js (Deprecated) Node.js (Neo) ODBC PHP Python R Rust Swift Wasm Overview Deploying DuckDB-Wasm Instantiation Data Ingestion Query Extensions SQL Query Syntax Data Types Expressions Functions
TL;DR: DuckDB extensions can now be published as DuckDB Community Extensions. The repository makes it easier for users to install extensions using the INSTALL extension_name FROM community syntax. Extension developers avoid the burdens of compilation and distribution. To browse existing community extensions, visit the DuckDB Community Extensions documentation page. DuckDB Extensions Design Philoso
Installation Documentation Getting Started Connect Data Import JSON Files Multiple Files Parquet Files Partitioning Appender INSERT Statements Client APIs C++ CLI Dart Go Java (JDBC) Julia Node.js (Deprecated) Node.js (Neo) ODBC PHP Python R Rust Swift Wasm SQL Query Syntax Data Types Expressions Functions Constraints Indexes Meta Queries DuckDB's SQL Dialect Samples Configuration Extensions Core
TL;DR: The DuckDB team is very happy to announce that today we’re releasing DuckDB version 1.0.0, codename “Snow Duck” (anas nivis). To install the new version, please visit the installation guide. For the release notes, see the release page. It has been almost six years since the first source code was written for the project back in 2018, and a lot has happened since: There are now over 300 000 l
TL;DR: A fast, free, and open-source Modern Data Stack (MDS) can now be fully deployed on your laptop or to a single machine using the combination of DuckDB, Meltano, dbt, and Apache Superset. This post is a collaboration with Jacob Matson and cross-posted on dataduel.co. Summary There is a large volume of literature, e.g., 1 and 2, about scaling data pipelines. “Use Kafka! Build a lake house! Don
There are many database management systems (DBMS) out there. But there is no one-size-fits all database system. All take different trade-offs to better adjust to specific use cases. DuckDB is no different. Here, we try to explain what goals DuckDB has and why and how we try to achieve those goals through technical means. To start with, DuckDB is a relational (table-oriented) DBMS that supports the
TL;DR: DuckDB-Wasm is an in-process analytical SQL database for the browser. It is powered by WebAssembly, speaks Arrow fluently, reads Parquet, CSV and JSON files backed by Filesystem APIs or HTTP requests and has been tested with Chrome, Firefox, Safari and Node.js. You can try it in your browser at shell.duckdb.org or on Observable. DuckDB-Wasm is fast! If you're here for performance numbers, h
database system Query and transform your data anywhere using DuckDB's feature-rich SQL dialect Installation Documentation -- Get the top-3 busiest train stations SELECT station_name, count(*) AS num_services FROM train_services GROUP BY ALL ORDER BY num_services DESC LIMIT 3;
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