---
title: Introduction
description: Introduction to dlt
keywords: [introduction, who, what, how]
---

# Getting started

![dlt pacman](/img/dlt-pacman.gif)

## What is dlt?

dlt is an open-source Python library that loads data from various, often messy data sources into well-structured datasets. It provides lightweight Python interfaces to extract, load, inspect and transform the data. dlt and the dlt docs are built ground up to be used with LLMs: [LLM-native workflow](dlt-ecosystem/llm-tooling/llm-native-workflow.md) will take you pipeline code to data in a notebook for over [5,000 sources](https://dlthub.com/workspace).

dlt is designed to be easy to use, flexible, and scalable:

- dlt extracts data from [REST APIs](./tutorial/rest-api), [SQL databases](./tutorial/sql-database), [cloud storage](./tutorial/filesystem), [Python data structures](./tutorial/load-data-from-an-api), and [many more](./dlt-ecosystem/verified-sources)
- dlt infers [schemas](./general-usage/schema) and [data types](./general-usage/schema/#data-types), [normalizes the data](./general-usage/schema/#data-normalizer), and handles nested data structures.
- dlt supports a variety of [popular destinations](./dlt-ecosystem/destinations/) and has an interface to add [custom destinations](./dlt-ecosystem/destinations/destination) to create reverse ETL pipelines.
- dlt automates pipeline maintenance with [incremental loading](./general-usage/incremental-loading), [schema evolution](./general-usage/schema-evolution), and [schema and data contracts](./general-usage/schema-contracts).
- dlt supports [Python and SQL data access](general-usage/dataset-access/), [transformations](dlt-ecosystem/transformations) and supports [pipeline inspection](general-usage/dashboard.md) and [visualizing data in Marimo Notebooks](general-usage/dataset-access/marimo).
- dlt can be deployed anywhere Python runs, be it on [Airflow](./walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [serverless functions](./walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions), or any other cloud deployment of your choice.

To get started with dlt, install the library using pip (use [clean virtual environment](reference/installation) for your experiments!):

```sh
pip install dlt
```

:::tip
If you'd like to try out dlt without installing it on your machine, check out the [Google Colab demo](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing) or 
use our simple [marimo / wasm based playground](./tutorial/playground) on this docs page.
:::

## Load data with dlt from …

<Tabs
  groupId="source-type"
  defaultValue="rest-api"
  values={[
    {"label": "REST APIs", "value": "rest-api"},
    {"label": "SQL databases", "value": "sql-database"},
    {"label": "Cloud storages or files", "value": "filesystem"},
    {"label": "Python data structures", "value": "python-data"},
]}>
  <TabItem value="rest-api">

Use dlt's [REST API source](./tutorial/rest-api) to extract data from any REST API. Define the API endpoints you'd like to fetch data from, the pagination method, and authentication, and dlt will handle the rest:

```py
import dlt
from dlt.sources.rest_api import rest_api_source

source = rest_api_source({
    "client": {
        "base_url": "https://api.example.com/",
        "auth": {
            "token": dlt.secrets["your_api_token"],
        },
        "paginator": {
            "type": "json_link",
            "next_url_path": "paging.next",
        },
    },
    "resources": ["posts", "comments"],
})

pipeline = dlt.pipeline(
    pipeline_name="rest_api_example",
    destination="duckdb",
    dataset_name="rest_api_data",
)

load_info = pipeline.run(source)

# print load info and posts table as data frame
print(load_info)
print(pipeline.dataset().posts.df())
```
:::tip
LLMs are great at generating REST API pipelines!
* [Follow LLM tutorial](dlt-ecosystem/llm-tooling/llm-native-workflow.md) and start with one of [5,000+ sources](https://dlthub.com/workspace)
* Follow the [REST API source tutorial](./tutorial/rest-api) to learn more about the source configuration and pagination methods.
:::

  </TabItem>
  <TabItem value="sql-database">

Use the [SQL source](./tutorial/sql-database) to extract data from databases like PostgreSQL, MySQL, SQLite, Oracle, and more.

```py
from dlt.sources.sql_database import sql_database

source = sql_database(
    "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam"
)

pipeline = dlt.pipeline(
    pipeline_name="sql_database_example",
    destination="duckdb",
    dataset_name="sql_data",
)

load_info = pipeline.run(source)

# print load info and the "family" table as data frame
print(load_info)
print(pipeline.dataset().family.df())
```

Follow the [SQL source tutorial](./tutorial/sql-database) to learn more about the source configuration and supported databases.

  </TabItem>
  <TabItem value="filesystem">

The [Filesystem](./tutorial/filesystem) source extracts data from AWS S3, Google Cloud Storage, Google Drive, Azure, or a local file system.

```py
from dlt.sources.filesystem import filesystem

resource = filesystem(
    bucket_url="s3://example-bucket",
    file_glob="*.csv"
)

pipeline = dlt.pipeline(
    pipeline_name="filesystem_example",
    destination="duckdb",
    dataset_name="filesystem_data",
)

load_info = pipeline.run(resource)

# print load info and the "example" table as data frame
print(load_info)
print(pipeline.dataset().example.df())
```

Follow the [filesystem source tutorial](./tutorial/filesystem) to learn more about the source configuration and supported storage services.

  </TabItem>
  <TabItem value="python-data">

dlt can load data from Python generators or directly from Python data structures:

```py
import dlt

@dlt.resource(table_name="foo_data")
def foo():
    for i in range(10):
        yield {"id": i, "name": f"This is item {i}"}

pipeline = dlt.pipeline(
    pipeline_name="python_data_example",
    destination="duckdb",
)

load_info = pipeline.run(foo)

# print load info and the "foo_data" table as data frame
print(load_info)
print(pipeline.dataset().foo_data.df())
```

Check out the [Python data structures tutorial](./tutorial/load-data-from-an-api) to learn about dlt fundamentals and advanced usage scenarios.

  </TabItem>

</Tabs>

## Join the dlt community

1. Give the library a ⭐ and check out the code on [GitHub](https://github.com/dlt-hub/dlt).
1. Ask questions and share how you use the library on [Slack](https://dlthub.com/community).
1. Report problems and make feature requests [here](https://github.com/dlt-hub/dlt/issues/new/choose).