---
title: "Data Transformations"
slug: "data-transformations"
description: "Dataddo has built its platform to provide such transformative capabilities, ensuring data is not just available but is also accurate, useful, analytics-ready."
updated: 2026-07-11T08:52:32Z
published: 2026-07-11T08:52:42Z
canonical: "docs.dataddo.com/data-transformations"
---

> ## 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 Transformations

Dataddo offers various data transformation techniques. This article explores our data transformation features, helping you prepare data for dashboards and more complex transformations.

:::(Warning) (For detailed guides on data transformation configurations, refer to:)
* [Combine data](/docs/combine-data){target=`_blank`}
* [Metadata Inclusion](/docs/metadata-inclusion){target=`_blank`}
* [PII Exclusion and Hashing](/docs/pii-exclusion-and-hashing){target=`_blank`}
:::

## Multi-Layered Approach to Data Transformation
To offer a strategic balance between automation and flexibility, Dataddo has a multi-layered approach to data transformation. You can transform data on the
* [Extraction-level](/docs/data-transformations#extractionlevel-transformations)
* [Destination-level](/docs/data-transformations#destinationlevel-transformations)
* [Flow-level](/docs/data-transformations#flowlevel-transformations)

This structured method reduces the need for extensive transformations at the data warehouse or final destination stages. By tackling key transformation tasks early in the process, it ensures that data is consistently analytics-ready, regardless of its origin. This not only simplifies the data integration process but also accelerates the journey from raw data to actionable insights.
![Data Flows - Transformation](https://cdn.document360.io/084ed225-3f99-4644-a2da-39ca0cd5ef45/Images/Documentation/Data%20Flows%20-%20Transformation.png){height="" max-width="800"}

## Extraction-Level Transformations
Dataddo automatically performs extraction-level transformations, ensuring no-code, analytics-ready data. These include:
* [Data flattening](/docs/data-flattening){target="_blank"},
* [Data harmonization](/docs/data-harmonization){target=`_blank`},
* [PII exclusion and hashing](/docs/pii-exclusion-and-hashing){target="_blank"}, or
* [Metadata inclusion](/docs/metadata-inclusion){target="_blank"}.

For custom extraction requirements, Dataddo offers universal connectors like [JSON Universal Connector](/docs/json-universal-connector){target="_blank"} or [CSV Universal Connector](/docs/csv-universal-connector){target="_blank"}.

### Data Flattening
Raw data is commonly in **nested** or **hierarchical formats**. Dataddo simplifies nested data from APIs into a tabular format, enhancing the no-code user experience. This ensures a seamless no-code experience for users, allowing for quick and efficient data integration.

### Data Harmonization
The data harmonization process standardizes data formats, ensuring seamless integration into analytics tools. To address data discrepancies, Dataddo:
* Converts dates to consistent formats.
* Ensures that numerical values align with the correct {{glossary.data type}}.

### Metadata Inclusion
Dataddo offers adding meta-information like the [Dataddo extraction timestamp](/docs/metadata-inclusion#dataddo-extraction-timestamp){target="_blank"} to your data. Metadata is crucial for tracking Slowly Changing Dimensions (SCD) and understanding the lineage of data.

### PII Exclusion and Hashing
The option to exclude or encrypt **personally identifiable information (PII)** ensures data security and compliance.

You can opt to:
* Fully omit the inclusion of PII.
* Include PII and use hashing to convert it into a unique string of characters, allowing analysis without compromising privacy.

## Flow-Level Transformations
### Combine data
[Combine data](/docs/combine-data){target="_blank"} from multiple sources using:
* **SQL editor**: Merge and transform data from multiple sources using custom SQL queries. Recommended for high-volume datasets.
* **Data union**: Combines datasets from multiple ***{{glossary.data source}}s*** by selecting sources in the UI. It operates similarly to the `UNION` operator in SQL.

To use data union, ensure that all ***sources*** have identical schemas, including the same number of columns, column labels, {{glossary.data type}}s.

## Destination-Level Transformations
At the ***{{glossary.data destination}}***-level, transformations depend on the platform's capabilities.
* [Data warehouses](/docs/data-storages){target="_blank"} typically offer a broad array of transformation tools, from aggregations to pivots, thanks to their robust data processing capabilities.
* [Dashboarding applications](/docs/dashboarding-apps){target="_blank"} may emphasize visual transformations for analytical presentation.
 
While Dataddo delivers data in an analytics-ready format, users should familiarize themselves with their ***destination***'s transformation tools to maximize its utility.