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# Mammoth quick start: from a CSV file to a shared dashboard

> Upload a messy sales CSV, profile it with Explore Cards, clean it with three pipeline tasks, download it, then publish and share a dashboard.

This tutorial takes a small, messy sales file through the whole Mammoth workflow: upload, explore, clean, download, then publish a dashboard and share its link. You start in **Data** > **New dataset** and finish in the dashboard's **Share** panel. You need the Project Editor or Project Admin role in a project.

![Diagram of a pipeline: raw data passes through Filter, Standardize, and Calculate tasks to produce clean data](https://mammoth.io/docs/images/20260324_061100_Completepipelineworkflowdiagram.webp)

## Before you start

- A Mammoth account. To get one, [sign up for Mammoth](https://mammoth.io/docs/signup/).
- The Project Editor or Project Admin role in a project. A Project Viewer can't add data or tasks.
- A CSV file of sales orders, such as `sales_data_messy.csv`, with the columns `product_name` and `order_date`. It works best with:
  - product names written in several variations;
  - dates in inconsistent formats;
  - duplicate entries.

You finish with a clean dataset you can download, a three-task pipeline that cleans next month's file the same way, and a dashboard on the clean data. For a tutorial on built-in sample data instead, see [Build your first pipeline](https://mammoth.io/docs/learn/start/build-your-first-pipeline/).

## Step 1: Upload the sales CSV as a dataset

1. Click **Data** in the left sidebar to open the **Data Library**.
2. Click **New dataset**.
3. In the **New dataset** dialog, choose **Upload** (the default).
4. Drag your file into the drop area, or click **Upload file**.
5. Click **Submit**.

![Animation of the empty Data Library: clicking New Dataset, choosing Upload, and adding a file](https://mammoth.io/docs/images/20260302_152934_upload-file.webp)

Mammoth reads your CSV and creates a dataset in your project. The dialog closes right away, the file appears in the Data Library while it loads, and a progress header shows "Upload complete" when it's done. Uploads can be up to 50 MB. See [Upload files](https://mammoth.io/docs/learn/get-data-in/upload-files/).

![Data Library showing sales_data_messy.csv with 7 columns and 518 rows, a Preview, and an Upload complete message](https://mammoth.io/docs/images/20260408_155738_File_uploaded_data-lib.webp)

Open the dataset from the list to see it in a view. A view is the data editor where you explore and transform data.

## Step 2: Profile the sales columns with Explore Cards

Check the data's quality and patterns before you change anything. The Explore panel sits above the data grid. If it's hidden, click the **Explore** toggle in the action bar.

1. Click **Add card** and choose **product_name** in the **Explore a column** list. The card lists each distinct value with its row count. Notice the variations: "iPhone 13", "iphone 13" and "IPHONE 13" appear as separate values.
2. Add a card for **order_date** the same way. A date column shows a timeline of the orders. If the card treats **order_date** as text, the mixed formats are why. You can fix the type later with a Convert Column Type task.
3. Click **View Data Quality** in the footer of the view. The summary shows the **Score** out of 100, **Column completeness**, **Critical Issues** and **Recommendations**.
4. Click **View Details** for the full report.

![View of sales_data_messy.csv with Explore Cards for product_name, order_date, unit_price, and region above the data grid](https://mammoth.io/docs/images/20260408_155758_VIewwithcards.webp)

Double-click a value on a card to filter the grid to those rows. The filter shows as a pill in the card header. Remove it at any time, or turn it into a pipeline task. See [Explore cards](https://mammoth.io/docs/learn/explore/explore-cards/).

The sample file has three common real-world issues, which Step 3 fixes:

| Issue | Example |
| --- | --- |
| Product names in several variations | "iPhone 13", "iphone 13", "IPHONE 13" |
| Duplicate records | The same order appears more than once |
| Old orders | A mix of old and recent orders |

## Step 3: Standardise product names with Bulk Replace

Each cleaning step you add becomes a task in the view's pipeline and runs again on future data.

1. Click the **3-dot menu button** in the **product_name** column header.
2. Select **Bulk Replace**.
3. Open the **Group Suggestions** tab. Mammoth proposes groups of values that look like variants of one another, for example "iPhone 13", "iphone 13" and "IPHONE 13".
4. Click **Add suggestion** on each group you want, or **Create all Group Suggestions**. Each group's **Group Name** is the value that replaces its variants.
5. Click **Next** to review the groups.
6. Click **Apply**.

A **Bulk Replace** task now appears in the Pipeline panel on the left. The same task runs on any new data added to this dataset. See [Find and replace values](https://mammoth.io/docs/learn/clean-and-shape/find-and-replace/).

![Bulk Replace panel on product_name with Group Suggestions listing AirPods Pro, IPAD AIR, and an iPhone 13 group of 3 values](https://mammoth.io/docs/images/20260408_155818_Bulk_relplace_config.webp)

![Bulk Replace review step showing the iPhone 13 group with Previous and Apply buttons](https://mammoth.io/docs/images/20260408_155831_Bulk_replace_apply.webp)

![View with a Bulk Replace task on product_name in the Pipeline panel and standardized iPhone 13 values](https://mammoth.io/docs/images/20260408_155850_Pipeline_with_bulk_replace.webp)

## Step 4: Remove duplicate rows

1. Click the **+** button (tooltip "Add a task").
2. In the Transform menu, open **Filter, Label & Replace**.
3. Select **Remove Duplicate Rows**.
4. Under **Evaluate duplicates from**, keep **All columns** to compare every column.
5. Click **Apply**.

A **Remove Duplicate Rows** task appears under the first one, and the row count goes down. See [Remove duplicates and check data](https://mammoth.io/docs/learn/clean-and-shape/duplicates-and-checks/).

![Task picker opened from the plus button in the Pipeline panel, with the search box, the category list and the Filter, Label & Replace category expanded to show Conditional Filter and Remove Duplicate Rows](https://mammoth.io/docs/images/20261009_092921_Task_picker_filter_label_replace.webp)

Tasks run from top to bottom in your pipeline. You can drag tasks into a new order, and Mammoth runs the pipeline again in the new sequence.

## Step 5: Filter to recent orders with a Conditional Filter

1. Click **+**, then **Filter, Label & Replace** > **Conditional Filter**.
2. The filter opens in **SQL** mode. Switch the **SQL** / **Builder** toggle to **Builder**.
3. Set the column to **order_date**.
4. Set the condition to **is later than**.
5. Set the date to **2023-01-01**.
6. Click **Apply**.

Date conditions need a date column. If **order_date** is text, add a Convert Column Type task first (see [Column types](https://mammoth.io/docs/learn/clean-and-shape/column-types/)). The pipeline now has three tasks. See [Filter rows](https://mammoth.io/docs/learn/clean-and-shape/filter-rows/) for every condition.

![View with three tasks in the Pipeline panel: Bulk Replace, Remove Duplicate Rows, and Conditional Filter, leaving 349 rows](https://mammoth.io/docs/images/20260408_155908_three-task-in-pipeline.webp)

You built a three-task pipeline without writing code. Each task runs in order and you see the result straight away. The pipeline is saved with the view, so the same steps apply when new data arrives.

## Step 6: Check each task in the pipeline

1. Look at the **Pipeline** panel on the left of the view. Each task card shows its function and key settings.
2. Click a task to open its settings in the function panel, where you can change them.
3. To see the data as it was right after a task, hover the row count in the task card's footer (tooltip "Preview Data at this Task") and click it.
4. Click **Exit Preview** to go back.

![Previewing data at Task 2, Remove Duplicate Rows, with the Exit Preview bar and the task's configuration panel open](https://mammoth.io/docs/images/20260408_160139_Preview_task_with_remove_duplicate_config.webp)

Every change is listed as a task, so team members can follow your logic. The pipeline is:

- **Repeatable**: new batches of data run through the same tasks.
- **Readable**: every transformation is listed in the Pipeline panel.
- **Reusable**: you can copy tasks into another view.

See [How the pipeline works](https://mammoth.io/docs/learn/clean-and-shape/how-the-pipeline-works/).

## Step 7: Download the cleaned data as a CSV file

1. Click the download icon (⬇) in the view toolbar to open the **Save a copy** menu.
2. Keep **Download file** selected.
3. Under **Format**, select **CSV**.
4. Click **Download CSV**.
5. Open the file. Product names are consistent, duplicates are gone, and only recent orders remain.

![Save a copy popover with Download file mode and CSV selected, and the Download CSV button](https://mammoth.io/docs/images/20261102_save-a-copy-popover.webp)

The **Save a copy** menu has these choices:

| Option | What it does |
| --- | --- |
| **Download file** | Downloads a file in the format you pick |
| **New dataset** | Saves these rows as a new dataset in this project |
| **Email** | Sends the file once, now, to people in this workspace |
| **CSV** | Comma-separated text |
| **Excel** | An Excel workbook, up to 50,000 rows |
| **JSON** | For developers, apps and APIs |
| **More formats** | NDJSON, XML, YAML and Parquet |

See [Download data](https://mammoth.io/docs/learn/share-and-export/download/).

## Step 8: Build a dashboard from the clean view

1. Click **Dashboards** in the left sidebar, then **Create**.
2. On **What do you want to build?**, choose your view under **With my data**.
3. Pick a format: **Dashboard**, **Slides**, **Doc**, **Q&A** or **Explore**.
4. Build one of the ideas Mammoth suggests from your columns, or describe the dashboard you want, for example "sales dashboard showing revenue by region".
5. Refine it with the AI chat or by hand.

![Create page "What do you want to build?" with With my data and With a shape starting points](https://mammoth.io/docs/images/20261102_create-page.webp)

You can also start from the view itself: click **Publish** in the view toolbar to open the same create page. See [Create a dashboard](https://mammoth.io/docs/learn/dashboards/create/).

## Step 9: Publish and share the dashboard link

1. Save your changes in the dashboard editor. You can't publish with unsaved changes.
2. Click **Publish** in the header. The **Share** panel opens.
3. Click **Publish**, or **Publish & copy link** to publish and copy the link in one step.
4. In the **Access** menu, choose who can open the link.
5. Send the link.

| Access option | Who can open the link |
| --- | --- |
| **Sign-in required** | People who sign in to Mammoth: everyone in the project, plus people you invite by email |
| **Anyone with the link and password** | Anyone who has the link and the password (8 to 128 characters) |
| **Anyone with the link** | Anyone who has the link, with no sign-in |

By default, everyone in the dashboard's project has access. Your plan sets how many dashboards you can publish. See [Publish and share a dashboard](https://mammoth.io/docs/learn/share-and-export/share-a-dashboard/).

## What you did in this quick start

1. **Upload**: drag a file into the **New dataset** dialog.
2. **Explore**: add Explore Cards and check **View Data Quality**.
3. **Transform**: add tasks from the column menu or the **+** button, with no code.
4. **Reuse**: the pipeline applies to new data.
5. **Export**: download a copy, or send it to other systems.
6. **Share**: publish a dashboard on the clean data.

## Keep the data current after the quick start

- **Add next month's file** to the same dataset with **Add or Replace data**, and the pipeline cleans it too. See [Add or replace data](https://mammoth.io/docs/learn/get-data-in/add-or-replace-data/).
- **Export after every run** with **Export**, for example to Google Sheets or a database. See [Send data to a database or BI tool](https://mammoth.io/docs/learn/share-and-export/send-to-destinations/).
- **Email the view on a schedule** with a Messaging automation. See [Send messages](https://mammoth.io/docs/learn/automate/messages/).
- **Try it with your own data**: upload a real file from your work, explore it, build tasks for your actual business problem, and share the result with your team. Learn on a small file first, then apply the same steps to larger datasets.

## Get help while you learn

- **Support**: click the **Support** chat icon at the bottom of the left navigation to ask a question or report an issue.
- **Documentation**: open the account menu (tooltip "My account") in the left navigation and choose **Documentation**.
- **Troubleshooting**: see [Troubleshooting](https://mammoth.io/docs/learn/help/).

## Related

- [Build your first pipeline](https://mammoth.io/docs/learn/start/build-your-first-pipeline/): a step-by-step tutorial on built-in sample data, with task options and fixes.
- [Core ideas](https://mammoth.io/docs/learn/start/core-ideas/): how datasets, views, pipelines and tasks fit together.
- [Pick your path](https://mammoth.io/docs/learn/start/pick-your-path/): next steps for analysts, managers and technical users.
- [Explore cards](https://mammoth.io/docs/learn/explore/explore-cards/): every way to filter from a card.
- [How the pipeline works](https://mammoth.io/docs/learn/clean-and-shape/how-the-pipeline-works/): how tasks run, task previews and Draft mode.
- [Edit, reorder and copy tasks](https://mammoth.io/docs/learn/clean-and-shape/manage-tasks/): preview the data at a task and restore an earlier pipeline version.

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