--- 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 … 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. ::: 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. 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. 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. ## 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).

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