> 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/aws-s3.md).

# AWS S3

Zingg can use AWS S3 as a source and sink

## Steps to run zingg on S3

* Set a bucket e.g. zingg28032023 and a folder inside it e.g. zingg
* Create aws access key and export via env vars (ensure that the user with below keys has read/write access to above) export AWS\_ACCESS\_KEY\_ID= export AWS\_SECRET\_ACCESS\_KEY= (if mfa is enabled AWS\_SESSION\_TOKEN env var would also be needed )
* Download hadoop-aws-3.1.0.jar and aws-java-sdk-bundle-1.11.271.jar via maven
* Set above in zingg.conf spark.jars=//hadoop-aws-3.1.0.jar,//aws-java-sdk-bundle-1.11.271.jar
* Run using below commands

```bash
 ./scripts/zingg.sh --phase findTrainingData --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
 ./scripts/zingg.sh --phase label --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
 ./scripts/zingg.sh --phase train --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
 ./scripts/zingg.sh --phase match --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
```

## Model location

```
Models etc. would get saved in 
Amazon S3 > Buckets > zingg28032023 >zingg > 100
```


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.zingg.ai/0.4.0/connectors/aws-s3.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.