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# Core concepts

> Definitions of the seven core Mammoth concepts: Workspace, Project, Dataset, Batch, View, Pipeline, and Task, and how they nest and work together.

Every platform has its own vocabulary. In Mammoth, Mammoth uses seven core concepts: Workspace, Project, Dataset, Batch, View, Pipeline, and Task. They describe how Mammoth organizes data work.

This page is for new users. It defines each concept with examples and explains how they work together. To build a pipeline right away, go to the [Quick start](https://docs.mammoth.io/learn/getting-started/quick-start).

![Seven nested levels: Workspace, Project, Dataset, Batch, View, Pipeline, Task, each with its container and security level](https://docs.mammoth.io/api/v1/images/20260324_054812_mammoth-hierarchy-wireframe.jpg)

## Workspace: Your organization's container

A **Workspace** represents your entire organization's Mammoth environment. When you sign up, you create or join a Workspace. It's where billing and subscription are managed, users are invited and given permissions, global settings are configured, and all Projects live.

### Workspace characteristics

Your Workspace is tied directly to your subscription plan and billing. The Workspace Owner has full administrative control over the environment, including managing users, billing, and security settings. Members have defined roles—Owner or Member—with specific permissions for each level. Complete isolation from other Workspaces creates a secure data boundary for your organization. Each Workspace can contain multiple Projects, and you can see your storage allocation in the interface (like "1.49GB / 2.1GB used").

**Real-world analogy**: Like a Google Workspace or Slack Workspace—your company's dedicated environment.

### Workspace roles and permissions

For the complete table of what each role can do, see the [roles and permissions matrix](https://docs.mammoth.io/learn/security-overview/access-control/#roles-and-permissions-matrix).

**Workspace Owner** has complete administrative control including:

- Billing management and subscription plan changes
- Invite, remove, and manage all workspace members and their roles
- Assign users to projects, including projects they're not part of
- Manage workspace API tokens and external API keys

**Workspace Member** capabilities:

- Can create projects and view the Activity Log
- Can be added to projects by Project Admins
- Cannot invite users to the workspace
- Full collaboration capabilities within assigned projects

### When you work with a Workspace

You'll work with your Workspace during initial account setup, when adding or removing team members, managing billing and subscription, or viewing cross-project activity if you're an administrator.

![Workspace Settings General tab showing data usage, current plan, and the list of Projects](https://docs.mammoth.io/api/v1/images/20260410_081643_WorkspaceSettingspanel.jpg)

## Project: Isolated area within a Workspace

A **Project** provides an isolated area within your Workspace for a specific team, department, or initiative. Each Project contains Datasets specific to that project's purpose, Live Connections scoped to project security, shared team members with project-level permissions, and independent settings and configurations.

### Project data isolation and security

Projects provide security boundaries—one project cannot see another's data. Live Connection credentials are stored per-project, ensuring sensitive database access is controlled at the team level. Multiple users can work in the same project with defined permissions like the Project Admin or Project Editor roles.

**Real-world analogy**: Like folders in Google Drive, but with security boundaries.

### Project roles and permissions

**Project Admin** (assigned automatically to the user who creates the project):

- Rename or delete the project
- Add workspace members to the project, invite external users, and remove users
- Create, update, or delete Live Connections
- All Project Editor capabilities

**Project Editor**:

- Add, edit, or delete datasets, views, and pipelines
- Create or modify queries from Live Connections
- Cannot manage project settings or users

### Common Project organization patterns

Teams typically organize Projects by department ("Marketing", "Finance", "Operations"), by initiative ("Q4 Sales Analysis", "Customer Churn Study"), or by data source ("Salesforce Data", "Financial System Data").

Create Projects based on data access requirements, not just organizational structure. If two teams need the same database credentials, they should share a Project—separate Projects mean maintaining duplicate credentials.

![Data Library with the Projects menu open, listing Marketing, Samples, Sales_2026, and a New Project option](https://docs.mammoth.io/api/v1/images/20260324_055543_Projectsidebarnavigation.jpg)

You can also view the Project list in Workspace Settings.

![Workspace Settings General tab with a Projects table listing name, content, last updated, and users](https://docs.mammoth.io/api/v1/images/20260324_060029_project_list.jpg)

## Dataset: Standardized data container

A **Dataset** is the result of importing data into Mammoth—your working data in standardized form. When you upload a CSV, connect to a database, or pull from an API, Mammoth creates a Dataset complete with schema (column names, data types, validation rules), metadata (source information, import history, row counts), batch collection (one or more data versions), and audit trail (complete lineage tracking).

### Datasets are source-agnostic

Datasets are source-agnostic—CSV files, database queries, and API responses all become Datasets. Mammoth is schema-aware, understanding column types and validating data automatically. Datasets are versioned through multiple Batches, and data is stored persistently and securely in Mammoth's cloud infrastructure.

### Dataset operations

You can create Datasets using multiple sources, including file uploads, clipboard data, connectors, URLs, webhooks, sample files, or by generating data with AI.

Once created, you can manage data using batch operations such as combine, replace, remove, or suspend. Create Views to transform and explore your data, and export datasets to supported destinations after processing is complete.

![New Dataset dialog on the Generate with AI option, with a generated manufacturing sensor table and Create new dataset button](https://docs.mammoth.io/api/v1/images/20260408_154753_dataset_creaiton.jpg)

## Batch: Data version within a Dataset

Each time data is added to a Dataset, it creates a **Batch**. Batches enable version control by tracking data changes over time, comparison by analyzing differences between time periods, rollback to return to previous data states if needed, and audit trails maintaining complete history of data imports.

### Batch characteristics

Each Batch has a unique identifier and timestamp, preserved source information, tracked row count and metadata, and can be combined, replaced, removed, or suspended based on your needs.

### Batch operations explained

**Replace** mode means new data overwrites old—typical for monthly data refreshes. **Combine** mode appends new data to existing data, perfect for cumulative datasets. **Delete** removes a batch, while **Suspend** hides it from all associated views without deleting its data.

**Real-world analogy**: Like Git commits for your data—every import is tracked and reversible.

### Common Batch patterns

Monthly sales reports might have 12 batches per year, with each batch representing one month. Daily transaction feeds could have 365 batches per year in combine mode. One-time historical imports use a single batch with replace on refresh.

![Dataset panel for Multi-Store_Retail_Sales.csv with a Batches table listing three active batches of 51 rows each](https://docs.mammoth.io/api/v1/images/20260408_155041_Batch_combine.jpg)

Use Replace mode for reports that need only the latest data. Use Combine mode when building historical datasets over time. Choose based on your analysis requirements.

## View: Your interactive transformation workspace

A **View** is where you interact with your data. It displays all batch data combined by default, provides an interactive data grid for sorting and filtering, shows Explore Cards for automatic profiling, includes a Metrics Panel for calculations, and contains a Pipeline for building transformations.

### Views as the starting point for work

Every Dataset opens in a default View, but you can create multiple Views from one Dataset. Views are non-destructive—changes don't affect source data. Views operate in two modes: display-only changes (like renaming columns or formatting) are temporary and not saved to pipelines, while Pipeline Tasks create automated, reusable transformations.

### Display changes versus Pipeline Tasks

New users often confuse the two.

View-level changes (Display changes) modify how data is presented within a specific View. These changes are saved with the View and persist when the View is reopened, but they do not alter the underlying dataset. They are intended for readability, exploration, and analysis within that View only.

Pipeline Tasks transform the actual dataset. They create reusable, automated transformation logic that persists across data refreshes, can be reused across multiple Views, and are included in automation and production workflows.

**When to use each**: Use view level changes when you want to improve clarity, formatting, or interpretation for a particular View. Use Pipeline Tasks when creating reusable transformation logic for production workflows.

**Real-world analogy**: A View is like an Excel sheet with saved formatting, filters, and calculated columns.\
A Pipeline Task is like a Power Query or ETL operation that permanently reshapes the data before anyone opens the sheet.

![View with a Pipeline panel of three tasks (Bulk Replace, Remove Duplicate Rows, Conditional Filter) beside the data grid and Explore Cards](https://docs.mammoth.io/api/v1/images/20260408_155255_three-task-in-pipeline.jpg)

## Pipeline: Automated transformation sequence

When you perform transformations in a View, they're added to the **Pipeline** as Tasks. The Pipeline is sequential (tasks execute top-to-bottom in order), reusable (apply same logic to new data automatically), auditable (every task documented and reviewable), editable (modify, reorder, or remove tasks anytime), and previewable (see results at each task).

### Build once, run on every refresh

New data automatically flows through the same Pipeline, eliminating manual repetition and ensuring consistency.

### Pipeline execution modes: immediate and Draft Mode

**Immediate execution** (default) runs each task as you add it and shows results in real time, which suits iterative development. **Draft Mode** queues your changes as **Pending Updates** that run together when you click **Apply now**, which suits large datasets, complex workflows, and testing before committing changes.

**Real-world analogy**: Like a recipe—write once, cook many meals.

### Common Pipeline patterns

Data cleaning pipelines typically follow this pattern: Filter → Remove duplicates → Standardize values → Convert types. Data combination workflows use: Join datasets → Select columns → Calculate derived fields. Reporting preparation might include: Filter to period → Aggregate by group → Pivot for layout.

![Pipeline panel in reorder mode with a warning that reordering tasks may affect data, listing three tasks with drag handles](https://docs.mammoth.io/api/v1/images/20260408_155154_reorder-tasksinpipeline.jpg)

Start building pipelines in immediate execution mode to see results instantly. Once you've validated the logic, switch to Draft Mode when processing large datasets—this batches your changes so they run together.

## Task: Individual transformation operation

Each transformation you apply creates a **Task**—a single transformation operation within a Pipeline. Tasks are atomic (one operation per task like filter, join, or calculate), configurable (parameters editable via Function Panel), ordered (position in pipeline determines execution sequence), and previewable (click to see before/after data state).

### Task categories

The task picker opens from the **+** button (tooltip **Add a task**) and organizes functions into seven categories. **AI Powered** includes Generative AI, AI SQL Query, Extract Text, Bulk Replace, and Conditional Filter. **Filter Label & Replace** includes Conditional Filter, Remove Duplicate Rows, Show Top/Bottom Rows, Edit Cells, Label & Insert Values, Fill Missing Values, Bulk Replace, and Find & Replace. **Date Numeric & Text** includes date functions such as Add / Subtract Date Values, Math Function, Window function, Extract Text, Text Formatting, and Extract JSON. **Reshape Group & Pivot** includes Group & Pivot and Unpivot. **Column Management** includes Add Column, Copy Columns, Combine Columns & Text, Convert Column Type, Split Into Multiple Columns, and Remove Column. **Unify** includes Join, Lookup, and Send to Dataset. **Controls** includes Data Check and Checkpoint Alert. To describe a transformation in plain language instead, use **Describe your transformation** in the picker.

### Function Panel for configuring a Task

When editing a Task, the Function Panel appears showing task configuration options, real-time preview of results, AI-powered suggestions where available, and validation with error messages. This visual interface removes the need for coding.

**Real-world analogy**: Like a single operation in an Excel formula, but visual and reusable.

![Conditional Filter Function Panel set to keep rows where store_name is Fashion Central or Chic Boutique, with Apply button](https://docs.mammoth.io/api/v1/images/20260408_155131_filter_config.jpg)

## How concepts work together

The seven concepts nest inside one another, as the following example and table show.

### Complete workflow example with all seven concepts

This scenario uses all seven concepts:

1. **Workspace**: "Acme Corp" (your company)
2. **Project**: "Q4 Sales Analysis" (your team's area)
3. **Dataset**: "Salesforce Opportunities" (imported data)
4. **Batch**: "December 2024 Import" (this month's data)
5. **View**: "Sales Pipeline Analysis" (your workspace)
6. **Pipeline**: 5 Tasks transforming the data
7. **Tasks**: Filter → Remove Duplicates → Join → Calculate → Pivot

### Data flow summary

Data flows from source through Mammoth like this: Data Source → Import → Dataset (with Batches) → View (with Pipeline of Tasks) → Export/Dashboard.

![Data flow diagram: Data Source to Dataset with Batches, to View, to a Pipeline of five Tasks, to Export or Dashboard](https://docs.mammoth.io/api/v1/images/20260324_054951_Dataflowdiagramshowing.jpg)

### Concept hierarchy summary

This table shows how the concepts nest within each other:

| Level | Container | What it holds | Security boundary | Primary roles |
| --- | --- | --- | --- | --- |
| 1 | Workspace | Projects | Organization-level | Workspace Owner, Member |
| 2 | Project | Datasets, Live Connections | Team/department-level | Project Admin, Project Editor |
| 3 | Dataset | Batches | Data-level | Project members |
| 4 | Batch | Rows of data | Version-level | Project members |
| 5 | View | Pipeline | Transformation-level | Project members |
| 6 | Pipeline | Tasks | Workflow-level | Project members |
| 7 | Task | Single operation | Task-level | Project members |

## Common questions about core concepts

### Where is my data stored?

Your data is stored in your Workspace, organized by Project → Dataset → Batch, in Mammoth's cloud infrastructure.

### What's the difference between a Dataset and a View?

A Dataset is the imported data container—the raw or source data. A View is the interactive workspace where you explore and transform that data. One Dataset can have multiple Views, each with different transformation logic.

### Why do I need Projects?

Projects provide security boundaries and organization. The Marketing team can't see Finance's data, and Live Connection credentials are scoped to Projects for data security. This ensures sensitive information stays within appropriate teams.

### What happens to my Pipeline when I add new data?

New Batches automatically flow through the existing Pipeline. Your transformation logic is reused without any manual work—this is the core automation benefit of Pipelines.

### Can I have multiple Pipelines on the same Dataset?

Yes! Create multiple Views from one Dataset, each with its own Pipeline for different purposes. For example, one View for executive reporting and another for detailed analysis.

## Industry-specific examples

### Financial services

**Scenario**: Monthly regulatory reporting

```plaintext
Workspace: First National Bank
Project: SOX Compliance
Dataset: General Ledger Transactions
Batch: December 2024 (Replace mode)
View: Regulatory Report View
Pipeline: Filter → Categorize → Validate → Aggregate
Tasks: Remove test transactions, categorize by account type, validate amounts
```

### Manufacturing

**Scenario**: Production quality tracking

```plaintext
Workspace: Acme Manufacturing
Project: Quality Control
Dataset: Production Line Data
Batch: Daily production runs (Combine mode)
View: Quality Metrics Dashboard
Pipeline: Calculate defect rates → Flag outliers → Trend analysis
Tasks: Math functions for percentages, conditional filters for thresholds
```

### Hospitality

**Scenario**: Multi-property revenue analysis

```plaintext
Workspace: Hotel Chain Corp
Project: Revenue Management
Dataset: Property Management System Data
Batch: Nightly revenue reports (Replace mode)
View: Executive Revenue Dashboard
Pipeline: Combine properties → Calculate RevPAR → Compare periods
Tasks: Union operations, math functions, pivot tables
```

## Security and compliance summary

Mammoth's hierarchical structure provides multiple layers of security:

- **Workspace isolation**: Complete separation between organizations
- **Project boundaries**: Teams cannot access each other's data
- **Live Connection scoping**: Database credentials secured at project level
- **Role-based permissions**: Workspace Owner and Member roles, and Project Admin and Project Editor roles, with specific capabilities
- **Activity Log**: Workspace activity is recorded in the [Activity Log](https://docs.mammoth.io/learn/monitor/activity-log)

## Next steps

Now that you understand Mammoth's core concepts, you're ready to start using the platform.

**Quick start**: See these concepts in action by building your first workflow → [Quick start guide](https://docs.mammoth.io/learn/getting-started/quick-start)

**Roles and permissions**: Role-based access control in detail → [Roles and permissions matrix](https://docs.mammoth.io/learn/security-overview/access-control/#roles-and-permissions-matrix)

**Draft Mode**: Execution modes and performance → [Draft Mode in the Pipeline](https://docs.mammoth.io/learn/transform/understanding-the-pipeline#draft-mode)

**Your first pipeline**: Hands-on tutorial with transformations → [Build your first pipeline](https://docs.mammoth.io/learn/getting-started/first-pipeline)

**Glossary and limits**: Look up a term or check a limit → [Glossary](https://docs.mammoth.io/learn/glossary) and [Limits and quotas](https://docs.mammoth.io/learn/limits-and-quotas)

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