Skip to content
Learn › Start here › Core ideas: open the docs menu

Views, pipelines and tasks in Mammoth explained

A view is where you explore and transform a dataset, its pipeline is the ordered list of tasks that run again on new data, and a task is one operation such as a filter or a join. Open a dataset from the Data Library to work in its view; Project Editors and Project Admins add tasks. For the whole hierarchy, see Core ideas.

View: where you explore and transform data

A View is the data editor for a dataset. It shows all active batches together in a data grid, with Explore Cards above it, a metrics panel for calculations, and the Pipeline panel on the left.

Every dataset opens in a default view. You can create more views of it, each with its own pipeline, for example one for executive reporting and one for detailed analysis. So one dataset can have several pipelines. A view never changes the source data. See Views: create, rename and delete.

Display changes versus pipeline tasks

Display changesPipeline tasks
ExamplesSorting, hiding or renaming a column, formatting a number or date columnFilters, joins, calculations
What they changeHow the data looks in this view onlyThe view's output
SavedWith the view, but not as tasksAs tasks in the pipeline
On refresh, exports, dashboardsNot part of the outputRun again on refresh, feed exports and dashboards built on the view, and can be copied into another view

A display change is like formatting an Excel sheet; a pipeline task is like a Power Query step that reshapes the data before anyone opens the sheet. See Rename, format and sort columns.

View with a Pipeline panel of three tasks (Bulk Replace, Remove Duplicate Rows, Conditional Filter) beside the data grid and Explore Cards

Pipeline: steps that run again on new data

Each transformation you apply in a view is added to its Pipeline as a task. Like a recipe, you write it once and cook it many times: when you add new data, it runs through the same steps without manual work.

PropertyMeaning
SequentialTasks run top to bottom, each on the output of the one above
ReusableThe same tasks run again on new data
ReadableEach task card shows its function and key settings
EditableEdit, suspend, reorder, copy or delete tasks at any time
PreviewableSee the data as it was right after any task

Pipeline Version history keeps earlier versions. See Edit, reorder and copy tasks.

Pipeline panel in reorder mode with a warning that reordering tasks may affect data, listing three tasks with drag handles

Run tasks right away or in Draft mode

By default (Draft mode off), each task runs as soon as you apply it and the data grid updates. With Draft mode on, the tasks you add or change are queued, the Pipeline panel shows "Pipeline has Pending Updates", and the changes run together when you click Apply now. Turn Draft mode on from the More Pipeline options (...) menu.

Start with it off to see each result straight away; turn it on for large datasets or long sequences of changes.

Common pipeline patterns

PurposeTypical tasks in order
CleaningFilter, remove duplicates, standardise values, convert types
CombiningJoin datasets, select columns, calculate new fields
ReportingFilter to a period, group and aggregate, pivot for layout

Task: one operation in a pipeline

A Task is one operation, such as a filter, a join or a calculation: like one step in an Excel formula, but visual and reusable. Its position decides when it runs.

The Transform menu opens from the + button (tooltip "Add a task") or the Describe your transformation box, which searches the functions and always offers Ask in plain words with your text as its prompt. It has seven categories:

CategoryFunctions
AI PoweredAsk in plain words and Generative AI, plus shortcuts to Extract Text, Bulk Replace and Conditional Filter
Filter, Label & ReplaceConditional Filter, Remove Duplicate Rows, Show Top/Bottom Rows, Edit Cells, Label & Insert Values, Fill Missing Values, Bulk Replace, Find & Replace
Date, Numeric & TextAdd/Subtract Date Values, Extract Date Part, Financial Year, Calculate Date Difference, Math Function, Window Function, Obtain Large or Small Values, Extract Text, Text Formatting, Extract JSON
Reshape, Group & PivotGroup & Pivot, Unpivot
Column ManagementAdd Column, Copy Columns, Combine Columns & Text, Convert Column Type, Remove Column, Split Into Multiple Columns
UnifySend to Dataset, Join, Lookup, Repeat Rows
ControlsData Check, Checkpoint Alert

The function panel shows a task's settings. Many functions preview the result before you click Apply, and the panel tells you when a setting is missing or wrong. See Calculate with formulas and the other pages in Clean and shape data.

Conditional Filter Function Panel set to keep rows where store_name is Fashion Central or Chic Boutique, with Apply button
Was this page helpful?

Something wrong? support@mammoth.io