---
title: "How to send data from Amazon Redshift to MadKudu?"
slug: "how-to-send-data-from-amazon-redshift-to-madkudu"
updated: 2024-10-28T18:32:51Z
published: 2024-10-28T18:32:51Z
---

> ## Documentation Index
> Fetch the complete documentation index at: https://help.madkudu.com/llms.txt
> Use this file to discover all available pages before exploring further.

# How to send data from Amazon Redshift to MadKudu?

## **How to integrate Amazon Redshift with MadKudu**

If your data is hosted in Amazon Redshift, you will need to

1. export the data to an S3 bucket [with this format](/v1/docs/amazon-s3-how-to-format-and-send-data-to-madkudu-via-a-bucket).
2. grant MadKudu access to the bucket following [these instructions](/v1/docs/amazon-s3-giving-madkudu-access-to-an-s3-bucket).

and we'll be able to start pulling your data.

Note: If you're looking to export data for a Segment Warehouse in Redshift, please follow [these instructions](/v1/docs/segment-how-to-send-historical-data-to-madkudu-via-s3) instead.

## **Exporting your Redshift data to S3**

With Redshift, you can UNLOAD data directly to a S3 bucket. For the file format, please follow the [same spec as our flat file import.](/v1/docs/amazon-s3-how-to-format-and-send-data-to-madkudu-via-a-bucket)

## Event

Once you've mapped the data into the expected format, you can export it to S3 with a command like this

```sql
UNLOAD ("SELECT event_text, event_timestamp, contact_key, event_attribute_1, event_attribute_2 FROM [your_table]")
TO 's3://madkudu-data-in-[your_org_id]/event/event.csv'
CREDENTIALS 'aws_access_key_id=[your_access_key];aws_secret_access_key=[your_secret_key]'
HEADER
DELIMITER '~'
ADDQUOTES
ESCAPE
MANIFEST
GZIP
ALLOWOVERWRITE
PARALLEL ON
;
```

Where:

- `SELECT` should be modified to match your column headers names
- `CREDENTIALS` → will be provided by MadKudu in the app > integration > S3 (if using our S3 bucket, otherwise, enter the credentials of your bucket)

Please use **EXACTLY** the same following commands as it tends to create less file corruption issues:

- `HEADER` -> include the header
- `DELIMITER '~'` → delimits values with a ~(abc~efg~...)
- `ADDQUOTES` → add quotes around values (abc → "abc")
- `ESCAPE` → escape special characters with quotes
- `MANIFEST` → reads multiple files in parallel
- `GZIP` → compress result
- `ALLOWOVERWRITE` : replace file with new one instead of keeping all files
  - *Recommended* when
    - files are big
    - customer sending all the table everyday with the increments (versus just the increments)
  - *Not recommended* (have a pattern, e.g. the date) for the name of the files)
    - If want to keep historic, especially when customer only sends increments (if the pull fails at a moment, then the missed file is not lost)
- `PARELLEL ON` : write multiple files at a time to go faster

NOTE: with these settings, every time you execute this command the existing files will be overwritten.

## Contact

```sql
UNLOAD ("SELECT contact_key, email, attribute_1, attribute_2 FROM [your_table]")
TO 's3://madkudu-data-in-[your_org_id]/identify/identify.csv'
CREDENTIALS 'aws_access_key_id=[your_access_key];aws_secret_access_key=[your_secret_key]'
DELIMITER '~'
ADDQUOTES
ESCAPE
MANIFEST
GZIP
ALLOWOVERWRITE
PARALLEL ON
;
```

## Account

```sql
UNLOAD ("SELECT contact_key, account_key, account_attribute_1, account_attribute_1 FROM [your_table]")
TO 's3://madkudu-data-in-[your_org_id]/group/group.csv'
CREDENTIALS 'aws_access_key_id=[your_access_key];aws_secret_access_key=[your_secret_key]'
DELIMITER '~'
ADDQUOTES
ESCAPE
MANIFEST
GZIP
ALLOWOVERWRITE
PARALLEL ON
;
```