---
title: "Data Flows Overview"
slug: "overview-flows"
description: "Data integration made easy with Dataddo. Create a flow quickly and merge, combine, and orchestrate data from various online services to any desired destination."
tags: ["Data flow"]
updated: 2026-07-12T16:30:26Z
published: 2026-07-12T16:30:26Z
canonical: "docs.dataddo.com/overview-flows"
---

> ## 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 Flows Overview

A ***data flow*** is the connection between a [***__PROTECTED_0__***](/docs/overview-sources){target=`_blank`} or sources and a [***__PROTECTED_1__***](/docs/overview-destinations){target=`_blank`}.

Key considerations for data flows include the following:

**One Dataset, One Flow**
- For fixed-schema connectors like HubSpot Analytics, each dataset must have its own flow. This means that if you need to extract multiple datasets, such as contacts and deals, you’ll need a separate flow for each.

**[Multi-Account Extraction](/docs/multi-account-extraction){target=`_blank`}**
- If multiple accounts share the same schema (i.e., identical attributes and metrics), their data can be combined into **a single flow using [data union](/docs/data-union)**. For example, data from multiple Google Analytics accounts can be consolidated and extracted together.

## Creating a Data Flow
Dataddo de-couples the data extraction and data ingestion processes allowing more architectural flexibility. When you create a ***data flow***, you create M:N relationships between ***sources*** and ***destinations***.

Flexibility provided by ***data flows*** facilitates a broad spectrum of data architecture patterns and implementations, such as:
1. Straightforward [data integration into dashboards and BI tools](/docs/simple-data-integration-to-dashboards){target="_blank"}
2. Batch data ingestion through [ETL/ELT processes into data warehouses](/docs/ingestion-to-data-warehouses){target="_blank"}
3. Moving [large datasets into data lakes](/docs/batch-ingestion-to-data-lakes){target="_blank"}
4. [Replicating databases](/docs/database-replication){target="_blank"}
5. Data activation via reverse ETL

## Types of Data Flow
### Batch Data Flow
Standard and default type of the flow. 

### Streaming Data Flow
When a Flow is configured to use streaming, the transfer of larger amounts of data is usually considerably faster. Additionally, with the Streaming Flow, you have the absolute guarantee that no data will be cached during the process. However, this also means that features requiring caching, such as flow-level data transformations, cannot be used.

## Data Flow Features
To further enhance flexibility and facilitate data management, Dataddo offers multiple features that can be configured on the ***flow*** level. These features can be divided in the following categories:

1. [Data transformation capabilities](/docs/overview-flows#data-transformation-capabilities)
2. [Data quality measures](/docs/overview-flows#data-quality-measures)
3. [Data backfilling](/docs/overview-flows#data-backfilling)

### Data Transformation Capabilities
Dataddo's multi-layered approach to [transformations](/docs/data-transformations){target="_blank"} ensures data is consistently analytics-ready from its origin to final destination.

Dataddo offers data transformation on the extraction-level (which are appplied automatically) and ***flow***-level. This structured method significantly reduces the need for extensive transformations at the data warehouse or other ***destinations***. 

### Data Quality Measures
To make sure [data quality](/docs/data-quality) is prioritized, Dataddo allows the configuration of **specific business rules** to either halt data writing operations or send notifications when discrepancies are detected.

This proactive approach ensures that only high-quality data is funneled into downstream systems, be it a data warehouse or a BI dashboard, thus safeguarding the reliability of insights derived.

### Data Backfilling
In cases of interruptions or changes, some data might be missing. To address such gaps, Dataddo offers [**data backfilling**](/docs/data-backfilling){target=`_blank`}.

Data backfilling allows you to retrieve and load missing data or historical data, no matter the ***destination***.

### Protecting Schema Integrity
When your ***data source*** [schema](/docs/schema){target=`_blank`} changes, the schema in your ***destination*** is NOT automatically altered to prevent unintended "domino" effects.

If a schema change in the ***source*** is detected, you will have to manually rebuild the ***source*** and then connect it to the relevant ***flow***. This measure ensures that all changes are deliberate, preserving the integrity of your data across all downstream systems.

## Other Data Flow Operations
### Edit a Data Flow
On the [Flows](https://app.dataddo.com/flows){target="_blank"} page, click on your ***data flow*** to see your ***flow*** details.

Generally, you will be able to:
* Change the ***data flow*** name
* Configure [data quality rules](/docs/data-quality)
* Change, [add](/docs/data-transformations){target=`_blank`}, or delete ***data sources***

If your ***destination*** is a [data warehouse](//docs/ingestion-to-data-warehouses){target=`_blank`}, you will be able to:
* Change [the write mode](/docs/data-storages#write-modes){target="_blank"}
* Change sync frequency
* Configure database table details

:::(Error) (Changing the write mode can cause data discrepancies!)
Make sure the write mode is the same for your ***flow*** as well as your data warehouse. Changing the write mode after the ***flow*** has been created might have unwanted effects.

Only proceed if you are aware how this will affect your data.
:::

### Delete a Data Flow
There are two ways to delete a ***data flow***.

1. Delete the ***data flow*** directly without deleting the connected ***data source(s)***.
    1. On the [Flows](https://app.dataddo.com/flows){target="_blank"} page, click on the trash can icon next to your ***flow***.
    2. Type **DELETE** to confirm and click on **Delete**.
2. Delete a **data source** which will delete all connected ***flows***.
    1. On the [Sources](https://app.dataddo.com/sources){target="_blank"} page, click on the trash can icon next to your ***flow***.
    2. A warning with all connected ***flows*** listed will pop up for confirmation.
    3. Type **DELETE** to confirm and click on **Delete**.

***
## Troubleshooting
### Database Table Was Not Automatically Created
If the ***flow*** to the database or data warehouse is **in a broken state right after the creation**, most commonly the **database table wasn't created**. Click on the three dots next to the flow and select **Show Logs** to look for the error description. In most cases the problem is one of these:

*   **Insufficient permissions**: Make sure that the service account you authorized access to destination has writing permissions (aka permissions to create a table).
*   **The table already exists**: Delete the existing table and restart the ***flow*** by clicking on **Manual data insert**.

### Flow Is Broken after Changing the Source
In order to maintain data consistency, Dataddo **does not propagate changes done at the flow level to the downstream database destinations** (i.e. table schemas are not automatically updated without your knowledge).

If your ***flow*** breaks after you changed your ***source*** schema, the updated schema most likely does not match the table that was already created in your ***destination***.
1. Click on the three dots next to the affected ***flow*** and choose **Show Logs** to look for the error description.
2. Delete the existing table in your database and reset the ***flow***. Dataddo will attempt to create a new table.
3. If the table cannot be deleted, manually add the missing columns to the existing table.

### Experiencing Data Duplicates
For ***destinations* that are primarily append-only**, the recommended approach is to use the **insert** write mode. However, this approach can result in duplicities in your data. To avoid that, consider other writing strategies:

*   **Truncate insert**: This write mode removes all the contents in your table prior to data insertion. 
*   **Upsert**: This write mode inserts new rows and updates existing ones. To perform this correctly, it is necessary to set a unique key representing one or multiple columns.

For more information on write modes, refer to [this article](/docs/data-storages#write-modes){target="_blank"}.

### Flow With UPSERT Write Mode Is Failing with *invalid_configuration* Message
The combination of columns that you have chosen **does not produce a unique index**. Edit the flow and include more columns to the index.