> For the complete documentation index, see [llms.txt](https://docs.coupler.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coupler.io/destinations/categories/database/redshift.md).

# Redshift

Amazon Redshift is a fully managed cloud data warehouse designed for large-scale analytics. It lets you run fast SQL queries across massive datasets, making it a natural home for operational data you want to analyze at scale.

Sending data to Redshift with Coupler.io means your warehouse stays up to date automatically — no manual exports, no ETL scripting required.

## Why use Redshift as a Coupler.io destination?

* **Centralize data from any source** — any Coupler.io source (Google Ads, Facebook, HubSpot, and more) can feed data directly into your Redshift tables
* **Automatic type enforcement** — Coupler.io detects column types and creates properly typed tables, so your queries and downstream models work as expected
* **Two write modes** — overwrite tables on each run for clean snapshots, or append rows for historical tracking
* **Scheduled refreshes** — once your data flow runs successfully, you can schedule it to keep Redshift in sync without lifting a finger

## Prerequisites

Before connecting, make sure you have:

* An active Amazon Redshift cluster (not paused)
* A database user with **CREATE** privileges at the database and schema levels, plus **INSERT** permissions on target tables
* Your cluster's **host**, **port** (default: 5439), **database name**, **username**, and **password** ready
* If your cluster is behind a firewall or VPC, allowlist these Coupler.io IP addresses: `34.123.243.115` and `34.170.96.92`

## Quick start

{% hint style="success" %}
Have your Redshift connection credentials open in another tab before you begin — you'll need host, port, database, username, and password.
{% endhint %}

{% stepper %}
{% step %}
**Create a data flow and add a source.** In Coupler.io, create a new data flow. Select your data source (e.g., Google Ads, HubSpot, PostgreSQL) and configure it by following the prompts to authenticate and choose the data entity you want to load.
{% endstep %}

{% step %}
**Select Redshift as your destination.** On the destination step, choose **Amazon Redshift** from the list.
{% endstep %}

{% step %}
**Add your Redshift account.** Click **+ Add Account** and fill in your connection details:

* **Host** — your cluster endpoint (hostname or IP address)
* **Port** — default is `5439`
* **Database** — the name of your target database
* **User** — your Redshift username
* **Password** — your Redshift password

Click **Connect** to verify the connection.
{% endstep %}

{% step %}
**Set your schema and table.** Enter the schema name and table name where Coupler.io should write data. If the schema or table doesn't exist yet, Coupler.io will create them automatically.
{% endstep %}

{% step %}
**Choose a write mode.** Select how Coupler.io should write data on each run:

* **Replace** — drops and recreates the table with fresh data every run; best for clean, current snapshots
* **Append** — adds new rows below existing data; best for building historical records
  {% endstep %}

{% step %}
**Run your data flow.** Click **Save and Run** to execute the first load. Check that rows appear in your Redshift table before setting up a schedule.
{% endstep %}
{% endstepper %}

## Supported features

| Feature              | Supported |
| -------------------- | --------- |
| Replace mode         | Yes       |
| Append mode          | Yes       |
| Automatic scheduling | Yes       |
| Type enforcement     | Yes       |
| Templates            | No        |


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