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Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
High-performance in-process analytical SQL database for fast queries
DuckDB is an embedded analytical database system designed for speed, reliability, and ease of use. Query CSV and Parquet files directly with rich SQL dialect support.
DuckDB is a high-performance analytical database system designed to run directly within your application process. Unlike traditional client-server databases, DuckDB operates in-process, eliminating network overhead and simplifying deployment for analytical workloads.
Built for data analysts, scientists, and engineers, DuckDB provides a comprehensive SQL dialect with advanced features including nested correlated subqueries, window functions, complex types (arrays, structs, maps), and native support for querying CSV and Parquet files directly. Deep integrations with pandas, dplyr, and other popular data tools make it a natural fit for existing workflows.
Available as a standalone CLI application or embedded library, DuckDB supports Python, R, Java, WebAssembly, and other languages. Its portable design runs on laptops, servers, or edge devices without external dependencies. The MIT license and active development community ensure transparency and extensibility for production use cases ranging from interactive data exploration to embedded analytics in applications.
When teams consider DuckDB, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Interactive Data Exploration
Analysts query multi-gigabyte Parquet datasets on laptops without loading data into separate databases, accelerating insight discovery.
Embedded Application Analytics
SaaS products embed DuckDB to provide customers real-time dashboards and reports without managing separate database infrastructure.
ETL Pipeline Processing
Data engineers transform CSV and Parquet files using SQL, replacing custom Python scripts with declarative queries for maintainability.
Jupyter Notebook Analysis
Researchers integrate DuckDB with pandas to run complex analytical queries on DataFrames, combining SQL power with Python flexibility.
DuckDB is optimized for analytical (OLAP) workloads with columnar storage and vectorized execution, while SQLite targets transactional (OLTP) use cases with row-based storage.
Yes, DuckDB can directly query CSV and Parquet files using standard SQL SELECT statements, eliminating separate import steps.
DuckDB provides clients for Python, R, Java, WebAssembly, and other languages, plus a standalone CLI application for all major operating systems.
Yes, DuckDB is production-ready for analytical workloads and embedded analytics, with MIT licensing and active development. It is not designed for high-concurrency transactional systems.
No, DuckDB runs in-process within your application, eliminating the need for server setup, configuration, or network communication.
Project at a glance
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