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Getting started


dlt is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. This guide will show you how to start using dlt with a simple example: loading data from a list of Python dictionaries into DuckDB.

Let's get started!


Install dlt using pip:

pip install -U dlt

The command above installs (or upgrades) the library core, in the example below we use DuckDB as a destination so let's add a duckdb dependency:

pip install "dlt[duckdb]"

Use a clean virtual environment for your experiments! Here are detailed instructions.

Make sure that your dlt version is 0.3.15 or above. Check it in the terminal with dlt --version.

Quick start

For starters, let's load a list of Python dictionaries into DuckDB and inspect the created dataset. Here is the code:

import dlt

data = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]

pipeline = dlt.pipeline(
pipeline_name="quick_start", destination="duckdb", dataset_name="mydata"
load_info =, table_name="users")


When you look at the code above, you can see that we:

  1. Import the dlt library.
  2. Define our data to load.
  3. Create a pipeline that loads data into DuckDB. Here we also specify the pipeline_name and dataset_name. We'll use both in a moment.
  4. Run the pipeline.

Save this Python script with the name and run the following command:


The output should look like:

Pipeline quick_start completed in 0.59 seconds
1 load package(s) were loaded to destination duckdb and into dataset mydata
The duckdb destination used duckdb:////home/user-name/quick_start/quick_start.duckdb location to store data
Load package 1692364844.460054 is LOADED and contains no failed jobs

dlt just created a database schema called mydata (the dataset_name) with a table users in it.

Explore the data

To allow sneak peek and basic discovery you can take advantage of built-in integration with Strealmit:

dlt pipeline quick_start show

quick_start is the name of the pipeline from the script above. If you do not have Streamlit installed yet do:

pip install streamlit

Now you should see the users table:

Streamlit Explore data Streamlit Explore data. Schema and data for a test pipeline “quick_start”.


dlt works in Jupyter Notebook and Google Colab! See our Quickstart Colab Demo.

Looking for source code of all the snippets? You can find and run them from this repository.

What's next?

Now that you have a basic understanding of how to get started with dlt, you might be eager to dive deeper. A great next step is to walk through our detailed tutorial, where we provide a step-by-step guide to building a pipeline that loads data from the GitHub API into DuckDB and teaches you how to use some of the most important features of dlt.

Follow the tutorial →

More resources:

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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