# Join data Coupler.io allows you to fetch data from several sources at the same time, and join them into a unified dataset - all with a single importer. Data **JOIN** is a term Coupler.io uses to describe the process of combining two or more sources using LEFT JOIN (from SQL terminology) - meaning, all the elements from the left set are kept while only matching elements from the right set are included {% embed url="" %} ### How to join data from multiple sources? Let's take this example to understand this feature further. We have these source tables: {% columns %} {% column width="58.333333333333336%" %} *Source 1: Contacts*

{% endcolumn %} {% column width="41.666666666666664%" %} *Source 2: Department*

{% endcolumn %} {% endcolumns %} **Goal**: We want to get a list of "Contacts" under the "Partner sales" department. Currently, the "Contacts" table only has the IDs. {% @arcade/embed flowId="l8nSlkOARJd5A6WlsqIz" url="" %} {% stepper %} {% step %} After adding the needed sources, go to the "Transformations" step and either select Join data from the data preview window, or hit + Add transformation >> Create Join option from the left sidebar {% endstep %} {% step %} Select the sources to join and the columns that will be used as the key: {% endstep %} {% step %} Click the "Join data" button and we will get the following results: {% endstep %} {% step %} After joining the data, you can apply [other transformations](https://docs.coupler.io/functionality/data-set/preview-data-and-data-sets) such as: hide and reorder columns, filter and sort data, add a formula column, etc. to achieve the desired result: {% endstep %} {% endstepper %} #### 1. Order of key source matters Let's review 2 sources {% columns %} {% column width="58.333333333333336%" %} *Source 1: Users data*

{% endcolumn %} {% column width="41.666666666666664%" %} *Source 2: Additional user data*

{% endcolumn %} {% endcolumns %} As we can see, both of the tables contain "Id" column representing the user id. But, together with that, amount of rows is different: 10 and 3 respectively * In case "Users data" is chosen as the Key source, we will receive the next result (10 rows as in the key source):

* In case "Additional user data" is chosen as the Key source, we will receive the next result (3 rows as in the key source):

#### 2. Join by multiple fields Along with Join by 2 sources, it is possible to join these sources by multiple fields {% columns %} {% column width="58.333333333333336%" %} *Source 1: Users data*

{% endcolumn %} {% column width="41.666666666666664%" %} *Source 2: Additional user data*

{% endcolumn %} {% endcolumns %} First, let's join these 2 sources by the "Id" field. The result will look like this (part of the data from "Additional user data" was cut due to missing references in the key source):

Next, we can join these 2 sources by one more column "Name".

The result of both joins will look like this (first column changed):

As a next step, we can clean up (filter out) results a bit and leave only rows that contain data. Also, we can hide some columns to have no duplicates.

And receive beautified results:

#### 3. Join by multiple sources **Goal:** We wanted to get a list of user names from the "Users" table, the amount of sold products from the "Transaction data" table, and product categories from the "Product data" table. {% columns %} {% column %} *Source 1: Users data*

{% endcolumn %} {% column %} *Source 2: Transaction data*

{% endcolumn %} {% column %} *Source 3: Product data*

{% endcolumn %} {% endcolumns %} Next data join setup will produce the desired result:

After applying data join along with the current transformation capabilities, the goal was achieved: