> For the complete documentation index, see [llms.txt](https://upsolver.gitbook.io/content/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://upsolver.gitbook.io/content/quickstarts-1/jobs/ingestion.md).

# Ingestion

Using familiar SQL syntax, you can create an ingestion job to read your data and write it into a staging table, or directly into a supported target. Upsolver ingestion jobs can automatically infer the schema, and populate the column names and types in the table.

Before ingesting your data, ensure that you have a connection to read from your data source. You will also need a metastore connection and corresponding cloud storage location for your staging table or a connection to your target system.

| Ingestion Job Basics                                                                                                                                                                     |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <p><a href="/pages/H7hg2F6sfCTqupoSpNSh">Ingest to a Staging Table</a><br>Learn how to copy data from Amazon S3 into a staging table in the data lake.</p>                               |
| <p><a href="/pages/BcEDinl0XNcFQSqaftMD">Output to a Target Table</a><br>Discover how to create a transformation job to copy data from a staging to a target table in the data lake.</p> |

| Stream and File Sources                                                                                                                                                                   |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <p><a href="/pages/KcCGGIu79iAqsJpI9o11">Amazon Kinesis</a><br>Find how to ingest data from an Amazon Kinesis stream into a staging table in the data lake or directly to the target.</p> |
| <p><a href="/pages/vcrFIv4fCDuyrsxkj4XZ">Amazon S3</a><br>Learn how to ingest your data from Amazon S3 into a staging table in the data lake or directly to the target.</p>               |
| <p><a href="/pages/lb6Hvu41eciN0Cfus7n0">Apache Kafka</a><br>Discover how to ingest your data from Apache Kafka into a staging table in the data lake or directly to the target.</p>      |
| <p><a href="/pages/g0Wu72NaKz6PPzeRvF5P">Confluent Kafka</a><br>Learn how to ingest data from your Confluent Kafka source into the data lake or directly to the target.</p>               |

| CDC Sources                                                                                                                                                           |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <p><a href="/pages/AAn1gq7kzsbbQ7dXEGOq">Microsoft SQL Server</a><br>Discover how to ingest data from Microsoft SQL Server into a staging table in the data lake.</p> |
| <p><a href="/pages/XjwG1gQ5jcKjH5d8w70y">MongoDB</a><br>Learn how to ingest data from MongoDB into a staging table in the data lake.</p>                              |
| <p><a href="/pages/HziBB2CwrCk4RfufVD4I">MySQL</a><br>Find out how to ingest from MySQL into a staging table in the data lake.</p>                                    |
| <p><a href="/pages/QXw6ZCghqI0GgUC5nXPH">PostgreSQL</a><br>Learn how to copy data from PostgreSQL into a staging table in the data lake.</p>                          |


---

# 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://upsolver.gitbook.io/content/quickstarts-1/jobs/ingestion.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.
