[dlt](https://pypi.org/project/dlt/) is an open-source library that enables you to create a data pipeline in a Python script, replacing your glue code with robust, declarative 1-liners with schema evolution. dlt is the first Python library in this space, enabling you to run pipelines anywhere python runs - no redundant orchestrators, etc. ## [dlt Official Documentation](https://dlthub.com/docs/intro) ### dlt Advantages - Can be run in unprecedented places like Airflow, Cloud Functions, GitHub Actions, and more. - Removes complexity by integrating with your existing stack. - Empowers data teams and collaboration by allowing you to discover or prototype in notebooks, run in cloud functions, and deploy to production. - Rapid data exploration and prototyping with DuckDB. - No vendor limits, dlt is forever free. ### dlt Disadvantages - As a relatively new tool, the community is still growing and there may be fewer learning resources available compared to more established tools. ## dlt Learning Resources https://dlthub.com/docs/getting-started ## [dlt Recent Posts](https://dlthub.com/docs/blog) %% wiki footer: Please don't edit anything below this line %% ## This note in GitHub <span class="git-footer">[Edit In GitHub](https://github.dev/data-engineering-community/data-engineering-wiki/blob/main/Tools/Data%20Ingestion/dlt.md "git-hub-edit-note") | [Copy this note](https://raw.githubusercontent.com/data-engineering-community/data-engineering-wiki/main/Tools/Data%20Ingestion/dlt.md "git-hub-copy-note")</span> <span class="git-footer">Was this page helpful? [👍](https://tally.so/r/mOaxjk?rating=Yes&url=https://dataengineering.wiki/Tools/Data%20Ingestion/dlt) or [👎](https://tally.so/r/mOaxjk?rating=No&url=https://dataengineering.wiki/Tools/Data%20Ingestion/dlt)</span>