Data Sources

Mammoth has several ways to create a dataset: upload a file, paste from a spreadsheet, connect to a source, generate sample data with AI, import from a URL, load a built-in sample, or receive data through a webhook. All methods produce a dataset you can explore, transform, and visualize.

This section is for anyone adding data to Mammoth, from analysts loading a file to administrators setting up recurring feeds. If you are not sure which method to use, start with Dataset creation options. To load a file from your computer, start with File upload.

Ways to create datasets

Method	Best for	Size limit	Auto-refresh
Upload	Files from your computer	50 MB per upload	No
Paste from Clipboard	Quick tests, small datasets	Browser memory	No
Connectors	Recurring, large, or live data	Depends on the source	Yes (Dataset Refresh)
Generate with AI	Testing and prototyping	User-defined (max 500 rows)	No
From URL	File links	None stated; larger files take longer	No
Sample Files	Exploring Mammoth, training, demos	Fixed sample datasets	No
Webhooks	Event-driven external pushes	Payloads held until processed	Push-based; processed daily or on Refresh

Upload

Drag-and-drop or browse for files from your computer. Best for one-time analysis, ad-hoc exploration, and working with files you already have.

• Handles: CSV, Excel (.xlsx, .xls), TSV, XML, and other formats (PDF and images when enabled for your workspace) listed in Supported formats
• Size limit: 50 MB per upload; a folder counts as one upload (see limits and quotas)
• Best for: Exploring new data, one-time projects, partner-provided files

See File upload and Supported formats.

Paste from clipboard

Copy data directly from Excel, Google Sheets, or any spreadsheet—then paste into Mammoth. A quick way to get started with small datasets.

• Handles: Tab-separated or comma-separated text, such as cells copied from a spreadsheet
• Practical limit: Browser memory
• Best for: Quick tests, small datasets, ad-hoc requests

See Paste from clipboard.

Connectors (Live Connections)

Connections to databases, APIs, and business applications. Mammoth pulls data from the source into a dataset, and the Dataset Refresh automation can refresh it on a schedule.

• Best for: Recurring analysis, large datasets, production workflows

See Connectors for the catalog of supported sources, how to add a connector, and per-connector guides. If a connection fails, see Connection troubleshooting.

Generate with AI

Describe the dataset you need in natural language, and AI creates realistic sample data for you. No source data is required.

• Generates: Any data structure you describe
• Size: User-defined or AI-determined
• Best for: Testing pipelines, prototyping dashboards, training scenarios

See Generate with AI.

From URL

Provide a file URL (a public link or a signed URL), and Mammoth fetches the file and creates a dataset.

• Handles: .csv, .xlsx, .pdf, .zip, and more (see the full list in the guide)
• Best for: Public datasets, open data portals, shared cloud storage links

See Import from URL.

Sample files

Pick one of the sample datasets that come with Mammoth, grouped into Business, Technology, Finance & Real Estate, and Healthcare & Entertainment.

• Best for: Exploring Mammoth, training, and demos

See Sample files.

Webhooks

Receive data pushed directly into Mammoth from external systems via a unique webhook URL. Use it for event-driven pipelines.

• Handles: JSON or form data sent in a POST request from external applications or automation tools
• Dataset modes: Replace existing data each time held payloads are processed, or combine them with existing records (new webhooks start in Replace mode)
• Data shape: Each payload lands as a raw JSON row in a single text column
• Best for: Event data, third-party app integrations, automated data pipelines

See Webhooks.

Not sure which to use?

See Dataset creation options for a comparison, a decision framework, and common scenarios.

Manage batches and Views

After you create a dataset, these guides explain how its data and Views behave:

• Batch management: how batches work, Combine and Replace modes, and how to suspend or delete batches.
• Batch operations in detail: filtering by batch, automated batch workflows, scale, and examples.
• View management: create, name, duplicate, reset, and template Views, and control Auto-sync.

After dataset creation

Whichever method you used, you now have a dataset. Next steps:

• Explore the data. Mammoth provides Explore Cards, the Metrics Panel, Filter Pills, and a Data Quality Report. See Explore and discover.
• Transform the data. Build pipelines to clean, combine, and prepare your data using transformation functions or AI Prompt. See Transform data.
• Share or export. Export to Excel or CSV, create Live Links, send to destinations, or publish as a Dashboard. See Export and share.
• Automate refresh. For datasets from Live Connections, set up scheduled refresh with email notifications and activity logging. See Automations.