> AI agents: this is one page from Mammoth Analytics documentation. The index of all pages as Markdown is https://docs.mammoth.io/llms.txt. Append `.md` to any docs URL, or send `Accept: text/markdown`, to get Markdown.

# Getting started with Mammoth

> Start with Mammoth Analytics. Choose a learning path for your role, learn the five core concepts, and follow the quick start to build your first pipeline.

This section is for new Mammoth users, whatever their role. Start with the [Quick start](https://docs.mammoth.io/learn/getting-started/quick-start), or choose a role-based path in [Choose your path](#choose-your-path).

Mammoth lets business users prepare, transform, and share data independently, without waiting on IT.

Mammoth is a single platform for the data journey, from messy source data to automated pipelines and shareable dashboards.

[Video: Getting started with Mammoth](https://www.youtube.com/watch?v=VehQ0BtUO4s)

## The Mammoth data workflow: connect, transform, export

Mammoth handles the data workflow in one platform:

**Start**: Data that's messy, siloed, in multiple formats across systems\
**Middle**: Visual transformation without code through intuitive pipelines\
**End**: Clean datasets, automated workflows, and shareable AI-powered dashboards

![Three boxes in a row, Connect, Transform, and Export, joined by arrows to show the Mammoth workflow](https://docs.mammoth.io/images/getting-started/one_platform_journey.jpg)

### What Mammoth provides

**Accessibility**

Built for business users, not just technical teams. Visual, no-code tools.

**End-to-end capabilities**

- **Connect**: connectors for databases, applications, and cloud storage
- **Explore**: Automatic data profiling with Explore Cards
- **Transform**: Visual pipeline development with a library of functions
- **Analyze**: AI-powered transformations with natural language
- **Share**: Export anywhere or create AI dashboards

**Cloud-based security**

Mammoth is cloud-based, so there is no infrastructure to run. It is SOC 2 Type II and ISO 27001 certified. See [Security and compliance](https://docs.mammoth.io/learn/security-overview/).

Tip: start by automating one repetitive Excel workflow.

## Choose your path

Your role shapes your journey. Choose the path that fits your needs:

### Path 1: Data analyst

![Portrait of Sam, the example data analyst persona](https://docs.mammoth.io/images/getting-started/sam.png)

**You need**: Automated Excel workflows and reports without asking IT

**Key capabilities**:

- File upload and data exploration
- Visual transformations with no code required
- Excel-familiar functions and operations
- Automated reporting and scheduling

[Getting started as a data analyst](https://docs.mammoth.io/learn/getting-started/as-a-data-analyst)

### Path 2: Technical user

![Portrait of Jay, the example technical user persona](https://docs.mammoth.io/images/getting-started/jay.png)

**You need**: Repeatable workflows for your team and fewer support requests

**Key capabilities**:

- Live Connections to databases and APIs
- Advanced transformations and multi-dataset joins
- Pipeline templates for team collaboration
- API integration for automation

[Getting started as a technical user](https://docs.mammoth.io/learn/getting-started/as-a-technical-user)

### Path 3: Manager

![Portrait of Jordan, the example manager persona](https://docs.mammoth.io/images/getting-started/jordan.png)

**You need**: Team visibility and self-sufficient operations

**Key capabilities**:

- Team collaboration and sharing
- AI-powered dashboards for executives
- Governance and permissions management
- Usage monitoring and analytics

[Getting started as a manager](https://docs.mammoth.io/learn/getting-started/as-a-manager)

## Understanding core concepts

Five concepts underpin Mammoth:

![Five boxes joined by arrows in order: Workspace, Project, Dataset, View, Pipeline](https://docs.mammoth.io/images/getting-started/core_concepts.jpg)

### 1. Workspace, Project, and Dataset hierarchy

Your organizational structure for data management. Workspace ties to your subscription, Projects create secure boundaries for different initiatives, and Datasets hold your actual data. This hierarchy keeps data organized and secure with project-scoped access.

**Why it matters**: Prevents data sprawl and enables proper governance without complexity.

[Learn more about data hierarchy](https://docs.mammoth.io/learn/getting-started/core-concepts#workspace-your-organizations-container)

### 2. Views as your interactive workspace

Where data exploration and transformation begins. Views provide point-and-click interfaces before you commit to automation, combining spreadsheet familiarity with analytics.

**Why it matters**: Explore and validate transformations interactively before building automated workflows.

[Learn more about Views](https://docs.mammoth.io/learn/getting-started/core-concepts#view-your-interactive-transformation-workspace)

### 3. Pipelines as automated transformation

Sequential tasks that transform your data automatically. Build once and it applies to every data refresh, so your Excel workflow becomes repeatable.

**Why it matters**: Convert manual work into automated processes.

[Learn more about Pipelines](https://docs.mammoth.io/learn/getting-started/core-concepts#pipeline-automated-transformation-sequence)

### 4. Draft mode versus immediate execution

Choose when transformations apply to your data. Draft Mode lets you batch multiple changes for testing, while immediate execution applies each change as you make it.

**Why it matters**: Test complex logic safely before committing to large datasets.

[Learn more about Draft Mode](https://docs.mammoth.io/learn/transform/understanding-the-pipeline#draft-mode)

### 5. Export and share

Send data anywhere—BI tools, databases, dashboards, or downloads. Mammoth works alongside your existing tools.

**Why it matters**: Mammoth complements your existing tools.

[Learn more about exporting](https://docs.mammoth.io/learn/export-share/)

Learn Views and Pipelines first, because the rest builds on them. Then add Live Connections, then the AI features.

## Key features for business users

### Explore Cards for data understanding

Automatic visualization of every column with histograms, bar charts, and statistical distributions. Use them to understand data quality and patterns.

**Use Case**: Spot data quality issues immediately after upload—missing values, unexpected distributions, outliers—before spending time on transformations.

[Learn more about Explore Cards](https://docs.mammoth.io/learn/explore-discover/explore-cards-overview)

### Describe your transformation in plain language

The **Describe your transformation** box converts a natural language request into transformation tasks. Describe your goal without learning function names.

**Use Case**: Complex multi-task transformations without technical expertise—"Remove duplicates, standardize vendor names, then join with budget data."

[Learn more about AI capabilities](https://docs.mammoth.io/learn/transform/generative-ai/)

### Visual pipeline development

Drag-and-drop transformation sequencing with a library of functions. See each task, understand the logic, and modify it.

**Use Case**: Build repeatable workflows for recurring processes—monthly reports, weekly dashboards, daily data feeds.

[Learn more about pipelines](https://docs.mammoth.io/learn/transform/)

### AI-powered dashboards

Describe what you want to see and Mammoth generates a dashboard from your data, without BI tool training.

**Use Case**: Executive reporting, stakeholder updates, and client dashboards that you can share.

[Learn more about dashboards](https://docs.mammoth.io/learn/dashboard/)

### Live Connections

Connections to databases, applications, and APIs that refresh your data without manual file downloads.

**Use Case**: Scheduled reporting from sources such as Salesforce and databases, with automatic refresh scheduling.

[Learn more about Live Connections](https://docs.mammoth.io/learn/connectors/)

If you're coming from Excel, start with file uploads and Explore Cards. If you're coming from Alteryx or similar tools, go directly to Live Connections and advanced pipelines.

## Next steps and resources

### Immediate actions

**1. Choose your path**

Select the persona-based guide above that matches your role:

- Business Analyst: Automate Excel workflows
- Technical User: Enable team self-service
- Manager: Standardize team operations

**2. Quick start tutorial**

Build your first pipeline with sample data.

[Start the quick start tutorial](https://docs.mammoth.io/learn/getting-started/quick-start)

### Learning resources

**Core concepts**

Detailed explanations of Mammoth's data hierarchy, terminology, and platform fundamentals. Essential reading for understanding how everything connects.

[Read Core concepts](https://docs.mammoth.io/learn/getting-started/core-concepts)

**First pipeline tutorial**

Hands-on walkthrough with sample data covering data upload, exploration, transformation, and automation. 

[Build your first pipeline](https://docs.mammoth.io/learn/getting-started/first-pipeline)

### Need help?

**Support documentation**

Troubleshooting guides, FAQs, and detailed feature documentation. Search for specific questions or browse by category.

[Open the troubleshooting guides](https://docs.mammoth.io/learn/troubleshooting/)

**Contact Support**

Direct assistance from the Mammoth team for technical questions, implementation guidance, or account issues.

[Contact Support](mailto:support@mammoth.io)

**Schedule training**

Team onboarding sessions, customized training programs, and implementation workshops are available. Request a session.

[Request training](https://mammoth.io/book-demo/)

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