I wrote a few blog posts praising DuckDB. I use DuckDB many times for my personal projects. However, DuckDB did not come with a graphical user interface (UI). As a visual person, I would need to use an IDE like DBeaver for data exploration to write SQL. It was not a deal breaker to use DuckDB; I could either wrap my query in Python or R, or copy and paste a multiple-line query from a text editor to DuckDB CLI. I just want something simple and quick with DuckDB.
On March 12, 2025, DuckDB announced DuckDB v1.2.1 introduces the DuckDB Local UI, addressing the lack of a native user interface. After using it for a few hours, I discovered that DuckDB’s local UI is excellent for your data project.
What I really like the DuckDB Local UI
Simple Web UI
Launching the DuckDB is far more simple than I expected; once you install DuckDB, all we need to do is run
duckdb -ui
Then the browser will launch automatically and address will show the UI starting address; the default is http://localhost:4213/
You will not need to install any applications on your device, establish a DuckDB connection, or determine which parameters to use. The process is straightforward and intuitive.
DuckDB, an open source in-process database created for OLAP workloads, provides key advantages over more mainstream OLAP solutions: It's embeddable and optimized for analytics. It also integrates well with Python and is compatible with SQL, giving you the performance and flexibility of SQL right within your Python environment. This handy guide shows you how to get started with this versatile and powerful tool.
Author Wei-Meng Lee takes developers and data professionals through DuckDB's primary features and functions, best practices, and practical examples of how you can use DuckDB for a variety of data analytics tasks. You'll also dive into specific topics, including how to import data into DuckDB, work with tables, perform exploratory data analysis, visualize data, perform spatial analysis, and use DuckDB with JSON files, Polars, and JupySQL. Understand the purpose of DuckDB and its main functions
Notebook-like Code Editor
Jupyter Notebook is well-known among data scientists and analysts. The look and feel of DuckDB’s UI reminds me of using the Jupyter Notebook.
You must first create a notebook file and then type SQL into each cell. Once the results are displayed as a table, it feels like a mini BI tool; you can further explore the data by applying additional filters, sorting, and pivot logic.
On the right side, it generates a profile of your dataset, which includes some basic data visualisations such as a histogram, row count over time, and so on. Another nice feature is that it shows the percentage of rows with null values.
Explore DuckDB UI with Real Data
We have some sample data loaded by default from DuckDB. Let’s build another example by loading a dataset from Kaggle — Netflix Movies and TV Shows dataset (CC0: Public Domain)
Import Data
We can use the create table command and read data from a local csv file
Get Dataset Profile
We can run FROM netflix to get a table view, and on the right side it should the available columns and distribution.
Run SQL Query
Let’s write a query that I share in my other post, How to Build an AI Agent for Data Analytics Without Writing SQL
Can you get the total shows per director and sort by total shows in descending order for the top 3 directors?
We can use the same query that LLM generated and run it in the DuckDB UI.
🎉 Rapid and clean way to get the result.
Final Thought
DuckDB Local UI provides a simple and clean way to write SQL in DuckDB. This UI enhancement adds features that will encourage more people to use DuckDB. I hope this blog post gives you an idea of how easy it is to work with DuckDB’s new UI. This gives data professionals a quick and easy tool for data analytics work.
DuckDB’s local UI is awesome!!
Books Recommendation
DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you’ll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you’ll learn everything you need to get the most out of DuckDB—all through hands-on examples.
DuckDB, an open source in-process database created for OLAP workloads, provides key advantages over more mainstream OLAP solutions: It’s embeddable and optimized for analytics. It also integrates well with Python and is compatible with SQL, giving you the performance and flexibility of SQL right within your Python environment. This handy guide shows you how to get started with this versatile and powerful tool.
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