> ## Documentation Index
> Fetch the complete documentation index at: https://mage-staging.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Community Examples

> A collection of Mage projects and examples created by our community.

## Written Tutorials

### [How to Streamline Communication in Data Pipelines Using Mage](https://medium.com/@xiaoxugao/1ab2a885dd73)

In her post, Xiaoxu introduces the concept of a *communication lifecycle* for data pipelines. Proactive feedback to stakeholders is essential for establishing trust and building confidence in data. Xiaoxu demonstrates how Mage's [callbacks](/design/blocks/callbacks) can be used to automate tasks based on block status, reducing the communication lifecycle and establishing a healthy relationship between data owners and consumers.

### [Modern Data Engineering with Mage](https://www.analyticsvidhya.com/blog/2023/06/modern-data-engineering-with-mage-empowering-efficient-data-processing/)

Sri Nikitha walks through a demo of Mage: from installation to a sample pipeline that loads data from a NYC taxi dataset, transforms it, and exports to BigQuery.

## Video Tutorials

### How To Create Data Pipelines in Mage

Shashank walks through a high-level overview of how to create data pipelines using Mage.

<iframe width="560" height="315" src="https://www.youtube.com/embed/wk9PLudonbg" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen />

### End-to-end Data Engineering Project with Mage

Using Mage and Python for orchestration and ingestion, Darshil walks through how to take his dataset from source to visualization using a combination of popular tools, including Mage, GCS, BigQuery, and Looker Studio.

<iframe width="560" height="315" src="https://www.youtube.com/embed/WpQECq5Hx9g" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen />

### Building an ETL Pipeline with Mage

Arul discusses the process of constructing an ETL (extract-transform-load) pipeline in Mage. Starting from Netflix's top 100 movie dataset, Arul extracts data, transforms it to a useable format, ans loads it into a database for further analysis.

<iframe width="560" height="315" src="https://www.youtube.com/embed/Jvr4Jm97rxY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen />
