> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/0.4.0/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/0.4.0/connectors/pipes.md).

# Zingg Pipes

Zingg Pipes are an abstraction for a data source from which Zingg fetches data for matching or to which Zingg writes its output. This lets users connect to literally any datastore that has a Spark connector.

The pipe is an easy way to specify properties and formats for the Spark connector of the relevant data source. Zingg pipes can be configured through the config [JSON](/0.4.0/stepbystep/configuration.md) passed to the program by outlining the datastore connection properties.

Pipes can be configured for the data or the output attributes on the [JSON](/0.4.0/stepbystep/configuration.md).

Each pipe has the following attributes:

## name

A unique name to identify the data store.

## format

One of the Spark-supported connector formats. jdbc/avro/parquet etc.

## options

Properties to be passed to spark.read and spark.write.

Let us look at some common data sources and their configurations.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://docs.zingg.ai/0.4.0/connectors/pipes.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.