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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 format has become increasingly popular. Major data warehouse platforms such as Databricks, Snowflake, Google BigQuery and AWS have all announced or already implemented support for Iceberg tables. These
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 SQL queries from a terminal window, and several third-party tools offer more sophisticated UIs. The DuckDB CLI provides advanced feature
Spreadsheets Are Everywhere Is anything more polarizing for data folks than spreadsheets? Wait, don't answer that, we don't have time to talk about leading and trailing commas again… The fact is that spreadsheets are everywhere. It is estimated that there are over 750 million spreadsheet users, compared to just 20 to 30 million programmers. That includes all languages put together! There are a num
File Formats CSV Files Data is most commonly stored in human-readable file formats, like JSON or CSV files. These file formats are easy to operate on, since anyone with a text editor can simply open, alter, and understand them. For many years, CSV files have had a bad reputation for being slow and cumbersome to work with. In practice, if you want to operate on a CSV file using your favorite databa
Installation Documentation Getting Started Connect Data Import JSON Files Multiple Files Parquet Files Partitioning Appender INSERT Statements Client APIs Overview C C++ CLI Dart Go Java Julia Node.js (Neo) Node.js Python R Rust Swift Wasm Overview Instantiation Data Ingestion Query Extensions ADBC ODBC SQL Query Syntax Data Types Expressions Functions Constraints Indexes Meta Queries DuckDB's SQL
TL;DR: DuckDB extensions can now be published via the DuckDB Community Extensions repository. 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. DuckDB Extensions Design Philosophy One of the main design goals of DuckDB is simplicity, which – to us – implies
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
Installation Documentation Getting Started Connect Data Import JSON Files Multiple Files Parquet Files Partitioning Appender INSERT Statements Client APIs C++ CLI Dart Go Java Julia Node.js (Neo) Node.js Python R Rust Swift Wasm ADBC ODBC SQL Query Syntax Data Types Expressions Functions Constraints Indexes Meta Queries DuckDB's SQL Dialect Samples Configuration Extensions Iceberg ICU inet jemallo
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
Simple DuckDB is easy to install and deploy. It has zero external dependencies and runs in-process in its host application or as a single binary. Read more Portable DuckDB runs on Linux, macOS, Windows, and all popular hardware architectures. It has idiomatic client APIs for major programming languages. Read more Feature-rich DuckDB offers a rich SQL dialect. It can read and write file formats suc
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