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# Your first pipeline

> Build your first data transformation pipeline in Mammoth with step-by-step guidance from data upload to automated workflow.

This tutorial is for new Mammoth users. It walks you through building your first pipeline, from loading data to applying transformations.

By the end, you'll have a three-task pipeline that filters, standardizes, and calculates new values from the sample data. The same approach turns other data cleanup work into a repeatable pipeline.

![Diagram of a pipeline: raw data passes through Filter, Standardize, and Calculate tasks to produce clean data](https://docs.mammoth.io/api/v1/images/20260324_061547_Completepipelineworkflowdiagram.jpg)

## Before you begin

### What you'll learn in this tutorial

In this tutorial, you'll learn how to:

- Upload or connect to a data source
- Add your first transformation from the transform picker
- See results as you build
- Create multi-task pipelines that execute in sequence
- Save your work for automated future use

### Prerequisites for the tutorial

- A Mammoth account with Workspace access
- Sample data (Mammoth provides a sample file) or your own data

Each step includes a screenshot of what you should see and an expected result.

## Step 1: Load the Multi-Store Retail Sales data

Before building a pipeline, you need data to transform. You can start with a Mammoth sample file or your own file.

### Option A: Use the sample data (recommended for first time)

1. Open any Project in your Workspace
2. Click **New Dataset**
3. In the New Dataset dialog, click **Sample Files**
4. Select the **Multi-Store Retail Sales** dataset
5. Click **Create new dataset**. Mammoth creates a Dataset and opens a View.

![New Dataset dialog with Sample Files selected and the Multi-Store Retail Sales sample highlighted](https://docs.mammoth.io/api/v1/images/20260305_061015_upload_via_sample.jpg)

The sample file contains retail sales data, so you can learn pipeline basics without preparing your own data.

### Option B: Upload your own file

1. Click **New Dataset** in the Project
2. Select your Excel, CSV, or other supported file
3. Wait for the upload to complete
4. Mammoth creates a Dataset and opens a View

![New Dataset dialog on the Upload option with Upload file and Upload folder buttons and a file drop area](https://docs.mammoth.io/api/v1/images/20260305_061112_upload_file.jpg)

Start with the sample data for your first pipeline, then apply the same techniques to your own data.

### What the View shows after loading data

After you load data (sample or your own) and open the Dataset, your screen displays:

- **Data grid** showing your data like a spreadsheet
- **Pipeline panel** on the left (empty for now—this is where your transformations will appear)
- A **+** button (tooltip **Add a task**) that opens the transform picker
- **Explore Cards** above the data grid showing column distributions

![Freshly loaded View with an empty Pipeline panel showing only Source and the plus button, and Explore Cards above the data grid](https://docs.mammoth.io/api/v1/images/20261009_092922_View_empty_pipeline_explore_cards.jpg)

**Expected result:** The grid shows data rows and the Pipeline panel shows "Pipeline (0)". You're ready for Step 2.

## Step 2: Add a Conditional Filter task for high-revenue orders

This step filters the data to show only orders with a total revenue over 100.

### Open the transform picker

1. Click the **+** button (tooltip **Add a task**)
2. The transform picker opens with a search box (**Describe your transformation**) and the transformation categories
3. The categories are **AI Powered**, **Filter, Label & Replace**, **Date, Numeric & Text**, **Reshape, Group & Pivot**, **Column Management**, **Unify**, and **Controls**

![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://docs.mammoth.io/api/v1/images/20261009_092921_Task_picker_filter_label_replace.jpg)

### Navigate to the Conditional Filter function

1. Click the **Filter, Label & Replace** category
2. Scroll through the function list to find **Conditional Filter**, or type its name in the search box
3. Click **Conditional Filter**
4. The Function Panel opens at the bottom of your screen

### Configure the Conditional Filter

Set up the filter logic in the Function Panel:

1. **Column to filter**: Click the dropdown and select **total_revenue**(or similar revenue column)
2. **Condition**: Select **is greater than** from the operator dropdown
3. **Value**: Enter **100** in the numeric field
4. **Filter type**: Keep **Show** selected (this keeps rows matching your condition)

![Conditional Filter Function Panel set to keep rows where total_revenue is greater than 100](https://docs.mammoth.io/api/v1/images/20260305_053837_Filter_rule.jpg)

The Function Panel shows a clear summary of what will happen: "Show rows where total_revenue is greater than 100."

### Apply the Conditional Filter

1. Click the **Apply** button in the Function Panel
2. The Function Panel closes automatically
3. The Pipeline panel on the left now shows: **"1. Conditional Filter"**
4. Your data grid updates to show only orders with total_revenue over 100
5. Notice the row count at the bottom changes (you'll see fewer rows now)

![View with one Conditional Filter task in the Pipeline panel and the grid reduced to 20 rows](https://docs.mammoth.io/api/v1/images/20260305_053928_filter_applied.jpg)

### What the Conditional Filter did

You've created your first reusable transformation:

- The filter logic is saved in your pipeline as a task
- The pipeline executed automatically (because Draft Mode is off by default)
- Your data now shows only high-value orders
- Most importantly: this filter will automatically apply to any future data you add to this Dataset

**Expected result:** The Pipeline panel shows one task, and the data grid displays fewer rows than before, all with total_revenue values above 100.

## Step 3: Standardize product names with Bulk Replace

This step standardizes text values that have slight variations. Use **Bulk Replace** to detect and standardize variations in the `product_name` column.

---

### Open Bulk Replace

1. Click the **+** button

2. Go to **Filter, Label & Replace**

3. Select **Bulk Replace**

The Bulk Replace panel opens.

---

### Detect product name variations in Bulk Replace

1. Select **product_name** in the **Selected column** dropdown

2. Open the **Group Suggestions** tab

3. Under **Grouping Settings**, choose a grouping method:

   - **Smart**

   - **Similarity**: Groups based on calculated similarity

   - **Spelling Match**: Groups spelling variations

   - **Common Patterns**: Groups by shared word patterns

4. Review the group suggestions

Each group shows:

- Suggested grouped values

- The number of occurrences

- A **+** icon to create a replace rule

To proceed:

- Click **+** for individual groups, or

- Select **Create all Group Suggestions** to generate all rules at once.

You can also move values between groups and remove groups you don't want.

---

### Review and adjust the Bulk Replace groups

1. Edit the standardized value if needed

2. Remove any group that should not be merged

---

### Apply Bulk Replace

1. Click **Apply**

The system updates the data grid with standardized `product_name` values based on the groupings.

---

### Result of Step 3

The **Pipeline panel** now includes:

1. Conditional Filter

2. Bulk Replace

Your product names are now standardized.

![View with a Conditional Filter task and a Bulk Replace task on product_name in the Pipeline panel](https://docs.mammoth.io/api/v1/images/20260305_054426_filter_bulk_replace_two_task_applied.jpg)

## Step 4: Add a Gross_Sale column with Math Functions

This step creates a new column with calculated values: gross sales based on quantity and unit price.

### Open Math Functions

1. Click the **+** button
2. Navigate to the **Date, Numeric & Text** category
3. Select **Math Functions**
4. The Function Panel opens for mathematical operations

![Math Function panel with an expression box, Apply results into New Column option, and a New Column Name field](https://docs.mammoth.io/api/v1/images/20260305_061817_math_function.jpg)

### Configure the Gross_Sale calculation

1. **New Column Name**: Enter **"Gross_Sale"**
2. **Formula**: Build your calculation using column names and operators
   - Example: `quantity_sold * unit_price`
3. Review the formula preview showing sample calculations
4. Click **Apply**

![Animation of entering a math expression and a new column name, then applying the Math Function task](https://docs.mammoth.io/api/v1/images/20260324_092107_math_pipeline.gif)

Your pipeline now has three tasks, and the data grid shows your new Gross_Sale column with calculated values for every row.

![View with three tasks in the Pipeline panel and a new Gross_Sale column in the data grid](https://docs.mammoth.io/api/v1/images/20260305_062002_three_rules_view.jpg)

**Expected result:** The Pipeline panel displays three tasks, and scrolling right in the data grid reveals your new calculated column.

## How the three-task pipeline runs

Your pipeline is a sequence of three transformations that execute in order.

### Pipeline structure

```plaintext
Pipeline (3)                           ← Total number of tasks
├── 1. Conditional Filter              ← First task (executes first)
│   total_revenue> 100
│   Rows filtered
│
├── 2. Bulk Replace                    ← Second task (executes second)
│   product_name variations grouped
│   Result: Names standardized
│
└── 3. Math Functions                  ← Third task (executes last)
    Create Gross_Sale column
    Result: New column added
```

### How tasks execute

When you run the pipeline (or when it runs automatically), Mammoth:

1. Starts with your original data
2. Applies Task 1 (Conditional Filter) → creates intermediate result
3. Takes that result and applies Task 2 (Bulk Replace) → creates another result
4. Takes that result and applies Task 3 (Math Functions) → creates final output

This sequential execution means each task works with the output of the previous task—like a recipe where each task builds on the last.

### Task options in the Pipeline panel

Hover over any task in the Pipeline panel to see available options:

![Animation of hovering over a task in the Pipeline panel to reveal its options toolbar](https://docs.mammoth.io/api/v1/images/20260324_092508_Pipelinepanelwithhover.gif)

- **Edit**: Modify the task's configuration (reopens Function Panel)
- **Preview Data at this Task**: See what the data looks like after this specific task (without later tasks)
- **Paste below**: Add a new transformation below the selected task
- **Add note**: Document why you added this task (business logic notes)
- **Duplicate**: Create a copy of this task
- **Delete**: Remove the task from the pipeline
- **Suspend**: Suspend the task from pipeline
- **Rename**: Rename a task in the pipeline

### Reordering tasks in the Pipeline

You can drag and drop tasks to change execution order:

1\. Click on the **Reorder** icon from the top toolbar in the pipeline

2\. Hover over a task in the Pipeline panel

3\. Click and hold the drag handle (⋮⋮ icon)

4\. Drag up or down to the new position

5\. Release to drop

6\. Click on **Reorder Tasks**

**Important:** Mammoth validates dependencies automatically. If Task B uses a column created by Task A, you cannot move Task B before Task A. The system will show a warning and prevent the reorder.

![Animation of dragging tasks in the Pipeline panel to reorder them, with a warning that reordering may affect data](https://docs.mammoth.io/api/v1/images/20260324_090629_reorder-tasks_1-test.gif)


## Reviewing the results of the pipeline

### Before and after comparison

**Starting point:**

- All rows with various revenue values

- Brand values with variations

- No Gross_Sale calculation

**After your pipeline:**

- Only rows where total_revenue > 100

- Standardized product values

- New Gross_Sale column showing calculated totals

![Two stacked screenshots of the View: the source data with 51 rows above, and the transformed data with 20 rows and a Gross_Sale column below](https://docs.mammoth.io/api/v1/images/20260324_090814_Splitviewcomparison.jpg)

The screenshot compares the original data on top with the transformed data on the bottom (20 rows, clean names, and 20 columns including Gross_Sale).

### Verify the pipeline results

Use these methods to confirm everything worked correctly:

**Check the Metrics Panel** (right side):

- Shows row count changes
- Displays summary statistics
- Updates dynamically with each transformation

**Scroll through the data grid:**

- Verify total_revenue values are all above 100
- Check product_name for consistency
- Look at Gross_Sale calculations

**Use "Preview Data at this Task":**

1. Hover over any task in the Pipeline panel
2. Click on **Preview x rows** from any task
3. See exactly what the data looked like after that task
4. Useful for debugging multi-step pipelines

Checking each task in turn shows where an issue occurs if results don't match expectations.

## Saving and reusing the pipeline

Your pipeline is saved automatically.

### The pipeline is already saved

Every task you add gets saved immediately to this View. There's no separate "save" button to remember. This means:

- Your pipeline persists in this View permanently
- You can close the browser and come back later
- Other team members with View access see the same pipeline
- The pipeline won't be lost if your browser crashes

### What you can do with the pipeline

After you build a pipeline, you can:

**Apply to new data automatically:**\
When you add new batches of data to this Dataset, your pipeline runs automatically, applying the same transformations. This means next month's data gets filtered, standardized, and calculated exactly the same way, with no manual work.

**Create a dashboard:**

1. Click **Create** to open **What do you want to build?**
2. Pick your transformed data under **With my data**. Mammoth shows starting points based on your columns and dates, or you can describe what you want
3. Choose a starting point or describe your dashboard

**Export your transformed data:**

1. Click the **Export** button
2. Choose your format: Excel, CSV, live link, database, or cloud storage
3. Your transformed data exports ready for use in other tools

**Schedule automatic refresh:**

1. Set up automation to refresh your data on a schedule
2. Your pipeline runs automatically with each refresh
3. Transformed data stays current without manual intervention

![Export menu for View 1 listing Mammoth managed, Send to Dataset, Live link, Databases, and External destinations](https://docs.mammoth.io/api/v1/images/20260324_091329_ViewinterfaceshowingExport.jpg)

![Publish as Dashboard dialog showing three Dashboard Suggestions and a field to describe your own dashboard](https://docs.mammoth.io/api/v1/images/20260324_091403_ViewinterfaceshowingDashboard.jpg)

### Next steps after the first pipeline

Now that you understand pipeline basics, you can:

- **Try Describe your transformation**: Click **+**, describe what you want in plain English in the search box, and let AI build the task for you
- **Explore more transformations**: The transform picker has functions for date operations, text formatting, joining data, and more
- **Build production pipelines**: Apply these techniques to your real business data
- **Set up Automations**: Configure Automations to run pipelines on schedules

For large datasets, turn on Draft Mode before adding multiple tasks. This lets you build your entire pipeline first, then execute all tasks at once instead of processing after each addition.

## Common first-pipeline problems and solutions

These are the issues new users encounter most often.

### Problem 1: "my transformation didn't work"

**Symptoms:** You added a task, but the data didn't change

**Solution:** Check if Draft Mode is ON. When Draft Mode is enabled, tasks appear in the Pipeline panel but don't execute until you click **Apply now** at the bottom of the Pipeline panel. Look for a blue indicator on tasks—this means they're drafted but not executed.

![Pipeline with Draft mode ON, a Copy Columns task marked New, and a Pipeline has Pending Updates bar with Apply now](https://docs.mammoth.io/api/v1/images/20260305_062339_challenge1.jpg)

### Problem 2: "I don't see the column I need"

**Symptoms:** Your required column doesn't appear in dropdown menus

**Solution:** Check the pipeline task order. If Task B creates a new column, and Task A tries to use that column, Task A can't find it yet. Reorder your tasks so the column-creating task comes first. Alternatively, add the task that creates the column earlier in your pipeline.

### Problem 3: "data looks wrong after my transformation"

**Symptoms:** Results don't match expectations after applying a task

**Solution:** Use "Preview Data at this Task" to identify where things went wrong:

1. Hover over each task in sequence, click on **Preview X rows**
2. It will show **Preview Data at this task**
3. Find the task where data became incorrect
4. Edit that specific task to fix the issue

This technique lets you isolate problems in complex multi-task pipelines without deleting everything.

### Problem 4: "I want to undo a transformation"

**Symptoms:** You applied a task and want to remove it

**Solution:** Delete the task from the pipeline:

1. Hover over the task in the Pipeline panel and click on More options(three dot menu)
2. Click the **Delete** icon (trash can)
3. Confirm deletion
4. The pipeline re-executes without that task, and your data reverts to the previous state

### Problem 5: "the Function Panel is confusing"

**Symptoms:** You're not sure what parameters mean or how to configure a task

**Solution:** Every parameter in the Function Panel has contextual help:

- Hover over any field to see a tooltip explanation
- Click the **(?)** icon next to parameters for detailed help
- Use **Describe your transformation** in the transform picker if visual configuration feels overwhelming: describe what you want, and AI configures the task for you
- For each transformation in the Function Panel, click the **Help (book icon)** to open the corresponding documentation page for detailed guidance.

## Quick reference for first pipelines

### Most common first transformations

These are five transformations new users often need:

1. **Conditional Filter** - Remove unwanted rows based on criteria
2. **Bulk Replace** - Standardize text values with variations
3. **Convert Column Type** - Fix data types (text to number, text to date)
4. **Remove Duplicate Rows** - Clean duplicate records
5. **Math Functions** - Calculate new values from existing columns

### Pipeline best practices

Follow these principles for successful pipelines:

**Start with filters:**\
Reduce dataset size early by filtering out unnecessary rows. Smaller data makes later tasks faster.

**Use Draft Mode for complex workflows:**\
If you're adding 5+ tasks, turn on Draft Mode. Build your entire pipeline, then execute once. This also helps you think through the full transformation logic before processing.

**Add comments to tasks:**\
Document business logic, calculation formulas, or decision criteria. Future you (and your teammates) will appreciate the context.

**Test with small data first:**\
If you have a large dataset, build and test your pipeline on a small sample file first, then apply it to the full dataset once it's working.

---

You've built your first pipeline. The skills you practiced here (filtering data, standardizing values, and adding calculations) apply to many data preparation tasks. Next, read [Core concepts](https://docs.mammoth.io/learn/getting-started/core-concepts), or explore the [Transform section](https://docs.mammoth.io/learn/transform/).

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Source: https://docs.mammoth.io/learn/getting-started/first-pipeline.md · Updated: 2026-10-13