---
title: "Data Destinations Overview"
slug: "overview-destinations"
description: "Send data to any data destination with Dataddo. Send data to data warehouses, lakes, lakehouses, or back to source applications. Sync data in dashboarding apps."
tags: ["Reference", "Data destination"]
updated: 2026-04-15T11:37:25Z
published: 2026-04-15T11:37:25Z
canonical: "docs.dataddo.com/overview-destinations"
---

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

# Data Destinations Overview

A ***data destination*** is a location or system where data is sent for further processing, analysis, or storage. This can be:
* [Dashboarding applications](/docs/dashboarding-apps){target=`_blank`} (e.g. [Databox](/docs/databox){target="_blank"}, [PowerBI](/docs/power-bi){target="_blank"}, [Tableau](/docs/tableau){target="_blank"} or [Looker Studio](/docs/looker-studio){target="_blank"})
* [Data storages](/docs/data-storages){target=`_blank`}
    * On-prem or hosted databases (e.g. [MySQL](/docs/mysql-destination){target="_blank"}, [Postgres](/docs/postgres){target="_blank"}, [SQL Server](/docs/universal-sql-server){target="_blank"})
    * Cloud data-warehouses (e.g. [Amazon Redshift](/docs/redshift){target="_blank"}, [Snowflake](/docs/snowflake){target="_blank"}, [Azure SQL Database](/docs/azure-sql-database){target="_blank"}, [Google BigQuery](/docs/google-bigquery){target="_blank"})
    * Object storages or data lakes (e.g. [Amazon S3](/docs/s3), [Azure Blob Storage](/docs/azure-blob-storage){target="_blank"}, Google Cloud Storage)
* [Business applications](/docs/applications){target=`_blank`} (e.g. Salesforce, [ExactOnline](/docs/exact-online-destination){target="_blank"}, [Klaviyo](/docs/klaviyo-destination){target="_blank"})
* Or any other platform designed to receive and manage incoming data

{{snippet.AllConnectorsCallout}}

## Selecting the Optimal Data Destination
Understanding the various data destinations available and their corresponding benefits is key to establishing an efficient data management strategy. This guide offers insights into different use cases and the data destinations best suited for each. This will assist in aligning your data management decisions with your organization's objectives.

| Use Case | Destination |
| --- | --- |
| [Data visualization, reporting, and BI of simple and small data](/docs/simple-data-integration-to-dashboards){target="_blank"} | For datasets with fewer than ~100K rows not needing complex transformations, a dashboarding app such as [Databox](/docs/databox){target="_blank"}, [Tableau](/docs/tableau){target="_blank"}, [PowerBI](/docs/power-bi){target="_blank"}, or [Looker Studio](/docs/looker-studio){target="_blank"} is suitable. This avoids the need for an intermediate storage, optimizing data workflows. |
| [Data visualization, reporting, and BI of complex or large data](/docs/ingestion-to-data-warehouses){target="_blank"} | For tasks with large or intricate datasets, consider cloud data warehouses like [Snowflake](/docs/snowflake){target="_blank"}, [BigQuery](/docs/google-bigquery){target="_blank"}, [Databricks](/docs/databricks){target="_blank"}, or [Amazon Redshift](/docs/redshift){target="_blank"}. These platforms support significant data volumes and SQL transformations. They also integrate with many BI tools. |
| [Building a data lake](/docs/batch-ingestion-to-data-lakes){target="_blank"} | For constructing a data lake to store large, diverse raw datasets, consider storages such as [Amazon S3](/docs/s3){target="_blank"}, [Azure Blob Storage](/docs/azure-blob-storage){target="_blank"}, or Google Cloud Storage. |
| [Intermediate storage for complex ETL processes](/docs/batch-ingestion-to-data-lakes){target="_blank"} | For ETL tasks using systems like [Databricks](/docs/databricks){target="_blank"} or [Azure Synapse](/docs/azure-synapse){target="_blank"}, services like [Amazon S3](/docs/s3), [Azure Blob Storage](/docs/azure-blob-storage){target="_blank"}, or Google Cloud Storage can act as staging areas for raw data. |
| Data activation | To reintroduce enriched data into original applications, you can send data back to platforms like Salesforce, [HubSpot](/docs/hubspot-destination){target="_blank"}, or other CRM/ERP systems. |

:::(Warning) (Ready to get started? [Connect your first data destination now](/docs/how-to-connect-a-data-destination){target=`_blank`}!)
:::

## Destination Details

On the **[Destinations](https://app.dataddo.com/destinations){target="_blank"}** page, click on your ***data destination*** to see your **Destination Details** where you can
* Change your ***data destination*** name
* Select a different ***{{glossary.authorizer}}***

## Other Data Destination Operations
### Duplicate/Clone a Destination
1. Click on the **Clone Settings** button next to your ***destination***.
2. Proceed with the on-screen prompts.

### Delete a Destination
If you want to delete a ***data destination***, make sure you first delete all ***{{glossary.data flow}}s*** the ***destination*** is connected to.

To delete a ***data destination***:
1. Go to the [**Destinations**](https://app.dataddo.com/destinations){target="_blank"} page.
2. Click the bin icon for the relevant ***data destination***.
3. Type **DELETE** to confirm your action. 

## Broken Destination Operation
### Restart Destination
If a ***data destination*** enters a broken state (marked **red**), you can attempt to restart it. If successful, the ***destination*** becomes will start working again. If restarting fails, an error message will let you know where the issue is.

In most cases, you will need to [reauthorize your service](/docs/authorized-services#how-to-reauthorize-your-account){target="_blank"}.

## Related Articles
### Troubleshooting
[Error Reporting Best Practice](/docs/error-reporting)
[Troubleshooting](/docs/troubleshooting)