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# Prepare data in Mammoth: connect, clean and explore

> A tour of how you prepare data in Mammoth: the Data Library, connectors, the no-code pipeline, AI transformations and Explore Cards.
>
> Applies to: Where: Data › New dataset

Preparing data in Mammoth means bringing it into a project's **Data Library**, checking it with Explore Cards, and cleaning it in a view's pipeline, by hand or with AI. Start from **Data** > **New dataset** in the left sidebar. For what Mammoth is as a whole, see [What is Mammoth](https://mammoth.io/docs/learn/start/what-is-mammoth/).

## Find your data in the Data Library

Click **Data** in the left sidebar to open the project's **Data Library**. Its tabs are **Datasets**, **Connections**, **Automations** and **Parameters**. **Datasets** lists folders, datasets and views, with status chips and actions for several items at once.

**New dataset** adds data: upload a file or folder, paste from the clipboard, connect a source, import from a URL, load a sample file, generate data with AI, or receive rows through a webhook. See [Find your way around the Data Library](https://mammoth.io/docs/learn/organise/data-library/).

## Connect databases, apps and cloud storage

![New Dataset dialog with Connectors selected, showing tiles for Amazon Redshift, Apache Hadoop, Databricks, Dropbox, Google Ads, and BigQuery](https://mammoth.io/docs/images/20260324_053035_Connector_screen.webp)

To connect databases, apps, cloud storage and APIs instead of re-uploading files, click **New dataset** and choose **Connectors**, or open **Data** > **Connections** and click **New Connection**. A database opens its connection form; other sources open the connector workspace, where a chat asks for credentials or a sign-in. A saved connection belongs to the project. See [Connect a database or app](https://mammoth.io/docs/learn/get-data-in/connect-a-source/) and [Manage saved connections](https://mammoth.io/docs/learn/get-data-in/connections/).

Datasets from databases, apps and APIs refresh with a Dataset Refresh automation. Files from drives and file servers (Dropbox, Google Drive, SharePoint, SFTP and others) are saved once; a File Collection automation brings in new files. One pipeline can join data from several sources.

## See what is in each column with Explore Cards

In the Explore panel above the grid, **Add card** picks a column. The card shows distinct values (text), a distribution (numbers) or a timeline (dates), and Mammoth remembers your cards for the view.

![Animation of a Sales_2026 View with 51 rows and 19 columns, where the cursor hovers over a column header to open its Explore Card](https://mammoth.io/docs/images/20260324_052830_Explorecards.webp)

Select values, then choose **Filter** or **Exclude**, or double-click a value to filter to it. Explore filters are temporary; you can turn them into a pipeline task or a new view. Use cards right after an upload to spot missing values, odd spellings and outliers.

## Clean data in a visual, no-code pipeline

Open a dataset to work on it in a view, the data editor for a dataset. Each task you add, such as a filter, a join or a pivot, goes into the view's pipeline in the **Pipeline** panel on the left. Tasks run top to bottom and you can drag them into a new order, so the pipeline is a readable record of your logic.

![Animation of reordering tasks in the Pipeline panel beside the Sales_2026 data table](https://mammoth.io/docs/images/20260302_171356_reorder-tasks_1-test.webp)

The **+** button ("Add a task") opens the Transform menu. Its seven categories are **AI Powered**, **Filter, Label & Replace**, **Date, Numeric & Text**, **Reshape, Group & Pivot**, **Column Management**, **Unify** and **Controls**.

![Pipeline panel with Conditional Filter, Bulk Replace, Join, and Group & Pivot tasks, and the Cards Density menu set to Compact](https://mammoth.io/docs/images/20260324_052632_task_with_expand_collapse.webp)

Preview the data at any task, and edit or reorder tasks later. **Draft mode** queues several changes to run together with **Apply now**. See [How the pipeline works](https://mammoth.io/docs/learn/clean-and-shape/how-the-pipeline-works/).

## Describe a transformation and let AI build it

| Task | What it does |
| --- | --- |
| **Generative AI** | Writes new column values from a prompt: classify, enrich, translate or summarise rows. |
| **Ask in plain words** | Turns a plain-language request into a SQL query and runs it on the view. |
| **Conditional Filter** and **Extract Text** | Build their rule from a sentence you type. |
| **Bulk Replace** | Suggests groups of similar values, such as "iPhone 15 Pro", "iphone 15 pro" and "iPhone15Pro", to replace with one name. |

![Empty Describe your transformation box in the View toolbar, showing its placeholder text, above the task categories](https://mammoth.io/docs/images/20261009_092923_Describe_your_transformation_box.webp)

The **Describe your transformation** box in the action bar opens the Transform menu and searches its functions. It always offers **Ask in plain words** too, with your text carried into its prompt. AI tasks produce ordinary pipeline tasks you can inspect and edit, so you can classify comments or pull an amount out of a notes column without a formula. See [Use AI on your columns](https://mammoth.io/docs/learn/clean-and-shape/ai-on-columns/) and [Write SQL with AI](https://mammoth.io/docs/learn/clean-and-shape/sql-with-ai/).

## Related

- [Automate and share data](https://mammoth.io/docs/learn/start/automate-and-share-overview/): what happens after the data is clean.
- [Quick start](https://mammoth.io/docs/learn/start/quick-start/): try these steps on a small file.
- [Core ideas](https://mammoth.io/docs/learn/start/core-ideas/): how datasets, views and pipelines fit together.

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Source: https://mammoth.io/docs/learn/start/prepare-data-overview.md · Updated: 2026-11-08