# Overview

The **Pipeline** module in ReOrc enables you to orchestrate data workflows efficiently. Rather than manually running models to retrieve results, you can define pipelines that automate the entire data transformation process. Pipelines represent your data workflows, from which you can schedule periodic runs, manage dependencies between models, and monitor workflow status in real time.

In ReOrc, pipeline is a another type of asset that can contain multiple models. When you create a pipeline, ReOrc automatically queries the relationship between models - derived from data lineage - and visualizes them in a directed acyclic graph (DAG).

<figure><img src="/files/hL8nMLVoV9I98wQBc6OQ" alt=""><figcaption></figcaption></figure>

The **Pipeline** module is designed to provide:

* **A high-level view**: Pipelines provide an accessible, high-level visualization of the data workflow, allowing data practitioners and stakeholders to easily understand the workflow's objectives and outcomes.&#x20;
* **End-to-end validation**: Pipelines automatically trigger data tests for models and assets during each run as a form of regression analysis. This helps you identify any issues introduced when chaining together several transformations.
* **Simplified troubleshooting**: Results and logs are instantly displayed in the Pipeline Health dashboard, where you can inspect details of each task to tfarace errors and resolve issues efficiently.

## Key features

The module currently supports the following features:

* [**Modeling pipeline**](/pipeline/modeling-pipeline.md): Create pipelines from data models.
* [**Job**](/pipeline/job.md): Configure job and schedule run from pipelines.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.reorc.com/pipeline/overview.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.