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https://github.com/dlt-hub/dlt
data load tool (dlt) is an open source Python library that makes data loading easy 🛠️
https://github.com/dlt-hub/dlt
data data-engineering data-lake data-loading data-warehouse elt extract load python transform
Last synced: 10 days ago
JSON representation
data load tool (dlt) is an open source Python library that makes data loading easy 🛠️
- Host: GitHub
- URL: https://github.com/dlt-hub/dlt
- Owner: dlt-hub
- License: apache-2.0
- Created: 2022-01-26T09:51:04.000Z (almost 3 years ago)
- Default Branch: devel
- Last Pushed: 2024-10-29T08:56:53.000Z (10 days ago)
- Last Synced: 2024-10-29T10:05:00.106Z (10 days ago)
- Topics: data, data-engineering, data-lake, data-loading, data-warehouse, elt, extract, load, python, transform
- Language: Python
- Homepage: https://dlthub.com/docs
- Size: 99 MB
- Stars: 2,522
- Watchers: 19
- Forks: 166
- Open Issues: 171
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-starred - dlt-hub/dlt - data load tool (dlt) is an open source Python library that makes data loading easy 🛠️ (python)
README
data load tool (dlt) — the open-source Python library for data loading
Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,
or a GPT-4 assisted development playground—dlt can be dropped in anywhere.
🚀 Join our thriving community of likeminded developers and build the future together!
## Installation
dlt supports Python 3.8+.
```sh
pip install dlt
```
More options: [Install via Conda or Pixi](https://dlthub.com/docs/reference/installation#install-dlt-via-pixi-and-conda)
## Quick Start
Load chess game data from chess.com API and save it in DuckDB:
```python
import dlt
from dlt.sources.helpers import requests
# Create a dlt pipeline that will load
# chess player data to the DuckDB destination
pipeline = dlt.pipeline(
pipeline_name='chess_pipeline',
destination='duckdb',
dataset_name='player_data'
)
# Grab some player data from Chess.com API
data = []
for player in ['magnuscarlsen', 'rpragchess']:
response = requests.get(f'https://api.chess.com/pub/player/{player}')
response.raise_for_status()
data.append(response.json())
# Extract, normalize, and load the data
pipeline.run(data, table_name='player')
```
Try it out in our **[Colab Demo](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing)**
## Features
- **Automatic Schema:** Data structure inspection and schema creation for the destination.
- **Data Normalization:** Consistent and verified data before loading.
- **Seamless Integration:** Colab, AWS Lambda, Airflow, and local environments.
- **Scalable:** Adapts to growing data needs in production.
- **Easy Maintenance:** Clear data pipeline structure for updates.
- **Rapid Exploration:** Quickly explore and gain insights from new data sources.
- **Versatile Usage:** Suitable for ad-hoc exploration to advanced loading infrastructures.
- **Start in Seconds with CLI:** Powerful CLI for managing, deploying and inspecting local pipelines.
- **Incremental Loading:** Load only new or changed data and avoid loading old records again.
- **Open Source:** Free and Apache 2.0 Licensed.
## Ready to use Sources and Destinations
Explore ready to use sources (e.g. Google Sheets) in the [Verified Sources docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources) and supported destinations (e.g. DuckDB) in the [Destinations docs](https://dlthub.com/docs/dlt-ecosystem/destinations).
## Documentation
For detailed usage and configuration, please refer to the [official documentation](https://dlthub.com/docs).
## Examples
You can find examples for various use cases in the [examples](docs/examples) folder.
## Adding as dependency
`dlt` follows the semantic versioning with the [`MAJOR.MINOR.PATCH`](https://peps.python.org/pep-0440/#semantic-versioning) pattern.
* `major` means breaking changes and removed deprecations
* `minor` new features, sometimes automatic migrations
* `patch` bug fixes
We suggest that you allow only `patch` level updates automatically:
* Using the [Compatible Release Specifier](https://packaging.python.org/en/latest/specifications/version-specifiers/#compatible-release). For example **dlt~=1.0** allows only versions **>=1.0** and less than **<1.1**
* Poetry [caret requirements](https://python-poetry.org/docs/dependency-specification/). For example **^1.0** allows only versions **>=1.0** to **<1.0**
## Get Involved
The dlt project is quickly growing, and we're excited to have you join our community! Here's how you can get involved:
- **Connect with the Community**: Join other dlt users and contributors on our [Slack](https://dlthub.com/community)
- **Report issues and suggest features**: Please use the [GitHub Issues](https://github.com/dlt-hub/dlt/issues) to report bugs or suggest new features. Before creating a new issue, make sure to search the tracker for possible duplicates and add a comment if you find one.
- **Track progress of our work and our plans**: Please check out our [public Github project](https://github.com/orgs/dlt-hub/projects/9)
- **Contribute Verified Sources**: Contribute your custom sources to the [dlt-hub/verified-sources](https://github.com/dlt-hub/verified-sources) to help other folks in handling their data tasks.
- **Contribute code**: Check out our [contributing guidelines](CONTRIBUTING.md) for information on how to make a pull request.
- **Improve documentation**: Help us enhance the dlt documentation.
## License
`dlt` is released under the [Apache 2.0 License](LICENSE.txt).