# Fusioning your datasets

**Data Fusion** is a point-and-click method to merge two datasets without writing SQL. It’s suited for:

* Non-technical users who want a quick way to join two tables.
* Simple merges where you pick matching columns and choose a join type (e.g., Left/Right/Inner) or a Union.

{% hint style="warning" %}
If you need more than two datasets, advanced transformations, or custom SQL (subqueries, aggregations, etc.), you’ll need to use **Scripting** instead.
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### Why fusion your datasets?

1. Non-technical or first-time users can merge two datasets by simply choosing columns and join type—no SQL required.
2. Quick reference table merges (e.g., attaching a lookup table).
3. One-step union of two tables with identical schemas.
4. **Limitations:**
   * You can’t fuse more than two datasets in a single operation.
   * No subqueries, advanced aggregations, or multi-step transformations.
   * Especially for **Union**, both datasets must align in column structure.

### How to fusion datasets

1. Under **Data → Prepare → Data Fusion**, you can find the following window. 

<figure><img src="https://content.gitbook.com/content/VXyBWnsg0T2tHBl87viA/blobs/kJ0yIpwkQNRatTjsB8Ch/image.png" alt="" width="563"><figcaption><p><strong>Data → Prepare → Fusion</strong></p></figcaption></figure>

2. **Dataset 1:** Auto-selects the dataset you've already selected.
3. **Dataset 2:** Select the other dataset you want to merge from the dropdown.
4. **Join Type** (Left, Right, Inner, Union)
   * **Left Join**: Returns all rows from *Dataset 1* plus matching rows from *Dataset 2*.
   * **Right Join**: Returns all rows from *Dataset 2* plus matching rows from *Dataset 1*.
   * **Inner Join**: Returns rows only where matches exist in *both* datasets.
   * **Union**: Stacks the rows from both datasets on top of each other—requires matching column names and data types.

{% hint style="danger" %}
If column names or data types don’t align for **Union**, you’ll see an error like “Unable to perform data union because of dataset mismatch.”
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5. **Join Column:** The columns for matching records between the two datasets. You specify *join columns* so the system knows *how* to line up rows from Dataset 1 with rows from Dataset 2. Including or excluding columns (check #6) just decides *which* fields appear in the final result—it doesn’t dictate *how* the two datasets match up in the first place.

{% hint style="info" %}
Auto Suggest analyzes the two datasets for column similarities—often by name or type—and then automatically proposes which columns might be a good match for joining. This saves time, especially when there are many columns, because you don’t have to manually identify and select the corresponding columns each time.
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* **Auto Suggest**: Automatically guesses matching columns if they share names or certain patterns.
* **Exact Match**: Columns must match exactly (case-sensitive name match).
* **Fuzzy Match**: Looser matching; tries to align columns that are spelled similarly.

5. **Select Column (Included | Excluded):** Choose which columns from both datasets should appear in the final fused dataset. Use the arrows to move columns from *Included* to *Excluded*, or vice versa. Search for the required column or choose **"Select All"** to select all the columns listed.

{% hint style="info" %}
**Join Column vs Select Column**

Join Columns define the “key” or shared attribute that both datasets use to match their rows. For example, if one table has a column `customer_id` and the other has a column `cust_id`, you map these together so the system knows which customer in the first dataset corresponds to the same customer in the second dataset.

Select Columns decide which columns end up *visible* in the final merged dataset. Maybe you only need a few columns from each dataset for your analysis. This step doesn’t affect *how* the data is joined; it’s purely about *presentation* and *relevance* of columns in your resulting dataset.
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7. **Dataset Name:** Provide a unique name for the new fused dataset you’re creating.
8. **Cache Dataset in Memory?**: If enabled, improves performance by loading the fused dataset into in-memory storage for frequently accessed or smaller datasets.
9. Click on **Create New Dataset** to finalize your Fusion setup and generate the new dataset. This new dataset appears in your dataset list, ready for use in dashboards, queries, or further transformations.