[](https://gitlab.com/databand-ai/dbnd/pipelines)
 
 
# DBND
DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production.
DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes.
DBND simplifies the process of building and running data pipelines
from dbnd import task
```python
from dbnd import task
@task
def say_hello(name: str = "databand.ai") -> str:
value = "Hello %s!" % name
return value
```
And makes it easy to track your critical pipeline metadata
```python
from dbnd import log_metric, log_dataframe
log_dataframe("my_dataset", my_dataset)
log_metric("r2", r2)
```
## Getting Started
See our [documentation](https://www.ibm.com/docs/en/dobd) with examples and quickstart guides to get up and running with DBND.
## The Latest and Greatest
For using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest:
```shell script
pip install dbnd
```
If you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai
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"description": "[](https://gitlab.com/databand-ai/dbnd/pipelines)\n\n \n \n\n\n# DBND\n\nDBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production.\n\nDBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes.\n\nDBND simplifies the process of building and running data pipelines\nfrom dbnd import task\n\n```python\nfrom dbnd import task\n\n@task\ndef say_hello(name: str = \"databand.ai\") -> str:\n value = \"Hello %s!\" % name\n return value\n```\n\nAnd makes it easy to track your critical pipeline metadata\n\n```python\nfrom dbnd import log_metric, log_dataframe\n\nlog_dataframe(\"my_dataset\", my_dataset)\nlog_metric(\"r2\", r2)\n```\n\n## Getting Started\n\nSee our [documentation](https://www.ibm.com/docs/en/dobd) with examples and quickstart guides to get up and running with DBND.\n\n## The Latest and Greatest\n\nFor using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest:\n\n```shell script\npip install dbnd\n```\n\nIf you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai\n",
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