# Mammoth Analytics Documentation > AI-powered data preparation and analytics platform. Upload datasets, build reversible transformation pipelines, and create dashboards. Mammoth docs are available as Markdown: append `.md` to any docs URL, or send `Accept: text/markdown`. ChatGPT, Claude and Perplexity fetching a page for a user receive Markdown by default. Full text of every page: https://mammoth.io/docs/llms-full.txt Docs MCP server: https://mammoth.io/docs/mcp/ (streamable HTTP, no authentication; tools search_docs, get_doc, list_docs). Server card: https://mammoth.io/docs/.well-known/mcp/server-card.json ## Instructions for AI coding assistants - Base URL for the Mammoth API: `https://app.mammoth.io/api/v2` (`app` is the default server prefix; if your Mammoth runs on another prefix, use `https://.mammoth.io/api/v2`). The OpenAPI document is at `https://app.mammoth.io/api/v2/docs/openapi.json`. - Authentication: an API token sent as `Authorization: Bearer mm_...`. The token belongs to one workspace, so no workspace header is needed; an `X-WORKSPACE-ID` header that names a different workspace gets 401. `X-API-KEY` and `X-API-SECRET` are deprecated; do not use them in new code. The Python SDK takes `MammothClient(api_token=...)` (it looks up the token's workspace itself) and is async: use `async with` and `await` every call that reaches the API. - There is no JavaScript or TypeScript SDK. Use the REST API, the Python SDK (`pip install mammoth-io`), or the CLI (`pip install --no-cache-dir --upgrade mammoth-cli`, then check `mammoth --version`). - Install the CLI and its agent skill: `curl -fsSL https://raw.githubusercontent.com/EdgeMetric/mammothsdk/main/mammoth-cli/installers/mammoth-install.sh | sh` (or `pipx install mammoth-cli && mammoth skill install`) (installs the `mammoth-cli` skill for Claude Code, Codex and Cursor). Alternative: `npx skills add https://mammoth.io/docs`. - Discover the live command contract before writing code: `mammoth schema find ""` to find a command, `mammoth schema get ` for its input and blast radius, `mammoth capability list --output json` for the SDK method and API operation behind it. - For the SDK and curl, keep the token in an environment variable; do not paste tokens into prompts or commit them. The CLI does not read environment variables: sign in with `mammoth auth login` (browser: `--method oauth`, device code: `--method device`, or a pasted API token: `--method token`). - The Mammoth MCP server, for MCP clients that act on a workspace, is at `https://mcp.mammoth.io/mcp` and signs in with OAuth. Setup: `/docs/agents/mcp/`. The documentation-search MCP server is separate, at `https://mammoth.io/docs/mcp/`. - Product documentation for humans is under `/docs/learn/`; API, CLI and SDK reference under `/docs/build/`; agent setup under `/docs/agents/`. - A short summary of the API for agents: `https://mammoth.io/docs/api.txt`. ## Full text by section One file per top-level section, for agents with a small context window (everything: https://mammoth.io/docs/llms-full.txt). - [Agents](https://mammoth.io/docs/llms-full/agents.txt) - [Build](https://mammoth.io/docs/llms-full/build.txt) - [Guides](https://mammoth.io/docs/llms-full/guides.txt) - [Learn](https://mammoth.io/docs/llms-full/learn.txt) - [Migration](https://mammoth.io/docs/llms-full/migration.txt) ## Common tasks - [Mammoth for coding agents](https://mammoth.io/docs/agents.md): Install the Mammoth CLI and its bundled skill, then let Claude Code, Codex or Cursor discover, read and safely mutate your Mammoth data. - [Mammoth MCP server](https://mammoth.io/docs/agents/mcp.md): Connect an MCP client such as Claude, ChatGPT or VS Code to your Mammoth workspace at https://mcp.mammoth.io/mcp. Setup per client, OAuth sign-in, revoking access, and the full tool list. - [Mammoth API authentication](https://mammoth.io/docs/build/auth.md): Authenticate against the Mammoth API with an API token sent as a Bearer header. Base URL, credential storage, and how to sign in without a terminal. - [Mammoth API quickstart](https://mammoth.io/docs/build/quickstart.md): From nothing to a transformed, exported dataset in about five minutes, shown three ways—curl, the mammoth-io Python SDK, and the Mammoth CLI. - [Mammoth Analytics Python SDK (mammoth-io)](https://mammoth.io/docs/build/sdk.md): Install mammoth-io, create a client, work with Views, apply transformations and export results. Python 3.12 to 3.14. ## Agents - [Mammoth CLI in agents and CI](https://mammoth.io/docs/agents/ci.md): Sign in without a terminal, pass structured input, wait on jobs, read the JSON envelope, and clean up safely. Machine behaviour turns on by itself. - [Use Mammoth with Claude](https://mammoth.io/docs/agents/claude.md): Use Mammoth from Claude Code with the Mammoth CLI and skill, and search Mammoth docs from Claude, Claude Desktop or Claude Code through the docs MCP server. - [Mammoth for coding agents](https://mammoth.io/docs/agents.md): Install the Mammoth CLI and its bundled skill, then let Claude Code, Codex or Cursor discover, read and safely mutate your Mammoth data. - [Mammoth MCP server](https://mammoth.io/docs/agents/mcp.md): Connect an MCP client such as Claude, ChatGPT or VS Code to your Mammoth workspace at https://mcp.mammoth.io/mcp. Setup per client, OAuth sign-in, revoking access, and the full tool list. - [Mammoth CLI blast radius and safety](https://mammoth.io/docs/agents/safety.md): Six mutation classes and four confirmation policies. What each Mammoth command does to your data, and what it demands before it runs. - [The Mammoth agent skill](https://mammoth.io/docs/agents/skill.md): mammoth skill install puts a CLI playbook into your coding agent—authentication, discovery, structured input, job handling and confirmations. ## Build - [Mammoth API authentication](https://mammoth.io/docs/build/auth.md): Authenticate against the Mammoth API with an API token sent as a Bearer header. Base URL, credential storage, and how to sign in without a terminal. - [mammoth activity](https://mammoth.io/docs/build/cli/activity.md): All 2 mammoth activity commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth addon](https://mammoth.io/docs/build/cli/addon.md): All 6 mammoth addon commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth agent](https://mammoth.io/docs/build/cli/agent.md): All 44 mammoth agent commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth ai](https://mammoth.io/docs/build/cli/ai.md): All 5 mammoth ai commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth annotation](https://mammoth.io/docs/build/cli/annotation.md): All 5 mammoth annotation commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth auth](https://mammoth.io/docs/build/cli/auth.md): All 5 mammoth auth commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth automation](https://mammoth.io/docs/build/cli/automation.md): All 13 mammoth automation commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth batch](https://mammoth.io/docs/build/cli/batch.md): All 7 mammoth batch commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth billing](https://mammoth.io/docs/build/cli/billing.md): All 27 mammoth billing commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth browse](https://mammoth.io/docs/build/cli/browse.md): All 9 mammoth browse commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth capability](https://mammoth.io/docs/build/cli/capability.md): All 3 mammoth capability commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth client-app](https://mammoth.io/docs/build/cli/client-app.md): All 5 mammoth client-app commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth collection](https://mammoth.io/docs/build/cli/collection.md): All 21 mammoth collection commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth completion](https://mammoth.io/docs/build/cli/completion.md): All 2 mammoth completion commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth config](https://mammoth.io/docs/build/cli/config.md): All 4 mammoth config commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth connector](https://mammoth.io/docs/build/cli/connector.md): All 40 mammoth connector commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth context](https://mammoth.io/docs/build/cli/context.md): All 4 mammoth context commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth dashboard](https://mammoth.io/docs/build/cli/dashboard.md): All 167 mammoth dashboard commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth data-app](https://mammoth.io/docs/build/cli/data-app.md): All 12 mammoth data-app commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth dataset](https://mammoth.io/docs/build/cli/dataset.md): All 37 mammoth dataset commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth external-key](https://mammoth.io/docs/build/cli/external-key.md): All 4 mammoth external-key commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth file](https://mammoth.io/docs/build/cli/file.md): All 11 mammoth file commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth folder](https://mammoth.io/docs/build/cli/folder.md): All 10 mammoth folder commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth history](https://mammoth.io/docs/build/cli/history.md): All 4 mammoth history commands, each with its blast radius, confirmation policy and backing Python SDK method. - [CLI reference](https://mammoth.io/docs/build/cli.md): All 43 mammoth CLI command families (872 commands), each linking to its commands, blast radius and backing SDK method. - [mammoth job](https://mammoth.io/docs/build/cli/job.md): All 4 mammoth job commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth log](https://mammoth.io/docs/build/cli/log.md): All 2 mammoth log commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth notification](https://mammoth.io/docs/build/cli/notification.md): All 5 mammoth notification commands, each with its blast radius, confirmation policy and backing Python SDK method. - [Other commands](https://mammoth.io/docs/build/cli/other.md): 10 mammoth CLI commands from single-command families and 51 API-only operations with no CLI command, with blast radius and backing SDK method. - [mammoth parameter](https://mammoth.io/docs/build/cli/parameter.md): All 14 mammoth parameter commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth project](https://mammoth.io/docs/build/cli/project.md): All 25 mammoth project commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth recipe](https://mammoth.io/docs/build/cli/recipe.md): All 2 mammoth recipe commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth schedule](https://mammoth.io/docs/build/cli/schedule.md): All 5 mammoth schedule commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth schema](https://mammoth.io/docs/build/cli/schema.md): All 3 mammoth schema commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth skill](https://mammoth.io/docs/build/cli/skill.md): All 7 mammoth skill commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth snippet](https://mammoth.io/docs/build/cli/snippet.md): All 8 mammoth snippet commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth support](https://mammoth.io/docs/build/cli/support.md): All 117 mammoth support commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth template](https://mammoth.io/docs/build/cli/template.md): All 5 mammoth template commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth token](https://mammoth.io/docs/build/cli/token.md): All 3 mammoth token commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth trash](https://mammoth.io/docs/build/cli/trash.md): All 3 mammoth trash commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth user](https://mammoth.io/docs/build/cli/user.md): All 7 mammoth user commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth view](https://mammoth.io/docs/build/cli/view.md): All 146 mammoth view commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth webhook](https://mammoth.io/docs/build/cli/webhook.md): All 7 mammoth webhook commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth workflow](https://mammoth.io/docs/build/cli/workflow.md): All 31 mammoth workflow commands, each with its blast radius, confirmation policy and backing Python SDK method. - [mammoth workspace](https://mammoth.io/docs/build/cli/workspace.md): All 33 mammoth workspace commands, each with its blast radius, confirmation policy and backing Python SDK method. - [Mammoth CLI capability contract](https://mammoth.io/docs/build/contract.md): Every mammoth CLI command and API-only operation (923 rows across 43 families), generated from `mammoth schema list` and `mammoth capability list`. - [Mammoth board embedding](https://mammoth.io/docs/build/embedding.md): Embed a Mammoth board in your own page. Covers the three embed modes, the origin allowlist, the workspace signing secret and the nine REST operations. - [Mammoth errors and exit codes](https://mammoth.io/docs/build/errors.md): The CLI error envelope, the exit-code table, which failures are worth retrying, and the per-method rate limits that apply to every caller. - [Build with Mammoth](https://mammoth.io/docs/build.md): Everything you can do to Mammoth from code, in one place—the CLI, the Python SDK, and the REST API, plus the generated capability contract behind all three. - [Mammoth API pagination](https://mammoth.io/docs/build/pagination.md): Most Mammoth list endpoints page with limit and offset. Cursor pagination exists on exactly two of 471 operations. Measured against the v2 spec. - [Mammoth API quickstart](https://mammoth.io/docs/build/quickstart.md): From nothing to a transformed, exported dataset in about five minutes, shown three ways—curl, the mammoth-io Python SDK, and the Mammoth CLI. - [Mammoth Analytics Python SDK (mammoth-io)](https://mammoth.io/docs/build/sdk.md): Install mammoth-io, create a client, work with Views, apply transformations and export results. Python 3.12 to 3.14. ## Learn - [AI-powered workflows](https://mammoth.io/docs/guides/advanced/ai-powered-workflow.md): Choose between Generative AI, AI SQL Query, Bulk Replace and the AI Prompt bar, write prompts with consistent output, and run AI pipelines in production. - [API-driven workflows](https://mammoth.io/docs/guides/advanced/api-workflows.md): Trigger Mammoth automations through the REST API, poll for completion, fetch View data, and push results to external systems, with Python examples. - [Custom transformations](https://mammoth.io/docs/guides/advanced/custom-transforms.md): Build complex transformation logic in Mammoth by chaining tasks, using the Generative AI task, pre-processing in SQL, and two-pass patterns, with examples. - [Dataset lifecycle workflows](https://mammoth.io/docs/guides/advanced/dataset-workflows.md): Manage the dataset lifecycle in Mammoth with dev, staging and production separation, View versioning, change control, rollback plans and deprecation. - [Enterprise architecture patterns](https://mammoth.io/docs/guides/advanced/enterprise-patterns.md): Organize workspaces, projects and roles in large Mammoth deployments, standardize pipelines, share certified Views across teams, and use a governance checklist. - [Incremental processing](https://mammoth.io/docs/guides/advanced/incremental.md): Choose between full refresh and incremental processing in Mammoth, with patterns for time windows, append-only data, file intake, CDC and late-arriving data. - [Advanced](https://mammoth.io/docs/guides/advanced.md): Architecture patterns for complex, high-volume or multi-team Mammoth projects, including governance, validation, incremental processing, AI and API workflows. - [Multi-source joins](https://mammoth.io/docs/guides/advanced/multi-source.md): Join records from different systems in Mammoth using exact keys, normalized names, composite keys, match scoring and AI matching, plus schema reconciliation. - [Pull data from a REST API with the CLI](https://mammoth.io/docs/guides/advanced/rest-api-import.md): Connect a REST or Elasticsearch source with the mammoth CLI, sample it, create a dataset, and refresh it on a schedule. Needs mammoth-cli 2.2.73 or later. - [Data validation patterns](https://mammoth.io/docs/guides/advanced/validation.md): Add Data Check tasks, quality gates, quarantine Views and reconciliation checks to Mammoth pipelines, and keep audit evidence for regulated reports. - [How-to](https://mammoth.io/docs/guides/how-to.md): Short Mammoth walkthroughs, each with a video, that show how to do one thing from start to finish, such as pulling chosen fields out of a PDF. - [Tell it what to pull, in plain words](https://mammoth.io/docs/guides/how-to/pull-fields-from-a-pdf.md): Pull the invoice number, date, supplier and total out of a PDF as one clean row by describing the fields in plain words. A one-minute video and the steps. - [Guides](https://mammoth.io/docs/guides.md): Task-oriented Mammoth guides for building end-to-end workflows, connecting specific tool pairs, and designing advanced pipelines, governance and API automation. - [Integration guides](https://mammoth.io/docs/guides/integrations.md): Guides for Salesforce to Tableau, Salesforce to a data warehouse, and SharePoint client credentials, plus a list of Mammoth connectors by category. - [Salesforce to data warehouse integration](https://mammoth.io/docs/guides/integrations/salesforce-snowflake.md): Send cleaned Salesforce data to BigQuery, PostgreSQL, SQL Server or Redshift with Mammoth, on a daily refresh. Snowflake is a source only, not a destination. - [Salesforce to Tableau integration](https://mammoth.io/docs/guides/integrations/salesforce-tableau.md): Clean Salesforce opportunity data in Mammoth and hand it to Tableau by CSV, Live Link, a Mammoth-managed database, or your own warehouse. - [Set up SharePoint client credentials](https://mammoth.io/docs/guides/integrations/sharepoint-client-credentials.md): Register an app in Microsoft Entra ID and get the client credentials Mammoth needs to download files from SharePoint. - [AI dashboard creation use case](https://mammoth.io/docs/guides/use-cases/ai-dashboard-creation.md): Create a Mammoth dashboard by describing it in plain language, refine it with the Dashboard Assistant, keep its data fresh, and publish it to viewers. - [Batch processing workflows](https://mammoth.io/docs/guides/use-cases/batch-processing-workflows.md): Process recurring batch files in Mammoth: set up an intake folder, normalize formats, consolidate files, add quality gates, and deliver clean data downstream. - [Customer cohort analysis](https://mammoth.io/docs/guides/use-cases/customer-cohorts.md): Analyze customer behavior over time with RFM segmentation and lifetime value metrics built in Mammoth. - [Data quality monitoring use case](https://mammoth.io/docs/guides/use-cases/data-quality.md): Monitor data quality in Mammoth with the data quality report, Data Check tasks, fail actions, failure notifications and View Insights for anomaly detection. - [Financial reporting use case](https://mammoth.io/docs/guides/use-cases/financial-reporting.md): Automate month-end close, consolidate financial data, and create board-ready dashboards in Mammoth. - [Use cases](https://mammoth.io/docs/guides/use-cases.md): Eight Mammoth workflows for sales analytics, financial reporting, customer cohorts, dashboards, data quality, multi-source integration and batch processing. - [Multi-source integration](https://mammoth.io/docs/guides/use-cases/multi-source-integration.md): Build one master dataset in Mammoth from CRM, ERP, database, file and API sources: normalize schemas, deduplicate names, resolve conflicts and refresh. - [Operational dashboards use case](https://mammoth.io/docs/guides/use-cases/operational-dashboards.md): Connect Mammoth to an operational database, build KPI Views, create a scheduled-refresh dashboard with AI, and share it with floor teams on desktop or mobile. - [Sales analytics use case](https://mammoth.io/docs/guides/use-cases/sales-analytics.md): Build an automated sales reporting pipeline in Mammoth, from Salesforce opportunity data to a refreshed, shareable dashboard. - [Mammoth Analytics Documentation](https://mammoth.io/docs/index.md): Documentation for Mammoth Analytics. Start with the quick start, connect data, build pipelines and dashboards, or use the REST API, Python SDK, and CLI. - [Automation types](https://mammoth.io/docs/learn/automations/automation-types.md): Compare the four Mammoth automation types (Dataset Refresh, Data Consolidation, File Collection, Messaging) by trigger, source, destination, and schedule. - [Creating an automation](https://mammoth.io/docs/learn/automations/creating-an-automation.md): Steps shared by every Mammoth automation: open the Automations tab, choose a type, complete its steps, name it, and save. - [Data Consolidation](https://mammoth.io/docs/learn/automations/data-consolidation.md): Create a Data Consolidation automation that merges files uploaded to a source folder into a destination dataset using Combine or Replace. - [Dataset Refresh](https://mammoth.io/docs/learn/automations/dataset-refresh.md): Create a Dataset Refresh automation to re-fetch cloud-connected datasets on a schedule, with Combine or Replace refresh methods and parallel or sequential runs. - [Automations](https://mammoth.io/docs/learn/automations.md): Overview of Mammoth automations (Dataset Refresh, Data Consolidation, File Collection, Messaging), Auto-sync, pipeline governance, and Monitor. - [Managing automations](https://mammoth.io/docs/learn/automations/managing-automations.md): View automation details, suspend and resume, use Run now, edit, and delete automations, and read the Active, Suspended, and Error statuses. - [Messaging](https://mammoth.io/docs/learn/automations/messaging.md): Create a Messaging automation that emails Dataset Views as CSV attachments to a recipient list on a schedule, with a 100,000-row attachment limit. - [Scheduling](https://mammoth.io/docs/learn/automations/scheduling.md): Configure when automations run, with minute, hourly, daily, weekly, monthly, and yearly frequencies, start and end dates, and multi-dataset ordering. - [Triggers](https://mammoth.io/docs/learn/automations/triggers.md): Configure when automations execute using schedules, manual runs, file uploads, API calls, and webhook data pushes - [Azure Blob Storage connector](https://mammoth.io/docs/learn/connectors/azure-blob-storage-connector.md): Connect Mammoth to Azure Blob Storage with Azure app credentials (Tenant ID, Client ID, Client Secret) and import files from a storage account. - [Google BigQuery connector](https://mammoth.io/docs/learn/connectors/bigquery.md): Connect Mammoth to Google BigQuery with a service account JSON key: required roles, how to connect, and how to import a table or query as a dataset. - [Databricks connector](https://mammoth.io/docs/learn/connectors/databricks.md): Connect Mammoth to Databricks with a personal access token and a SQL warehouse HTTP Path, then import a table or SQL query as a dataset. - [Dropbox connector](https://mammoth.io/docs/learn/connectors/dropbox-connector.md): Connect Mammoth to Dropbox with OAuth, select files or folders, and import a supported file (CSV, Excel, XML, archives) as a dataset. - [Facebook Ads Manager](https://mammoth.io/docs/learn/connectors/facebook-ads-manager.md): Connect Mammoth to Facebook Ads Manager with OAuth, query your ad account tables with SQL, and save the results as a dataset. - [Google Cloud Storage connector](https://mammoth.io/docs/learn/connectors/google-cloud-storage-connector.md): Connect Mammoth to Google Cloud Storage with a service account JSON key, pick a file from a bucket, and refresh it by name pattern or as the same file. - [Google Drive connector](https://mammoth.io/docs/learn/connectors/google-drive.md): Connect Mammoth to Google Drive with OAuth, pick files or folders in Google's file picker, and import a supported file as a dataset. - [Google Sheets connector](https://mammoth.io/docs/learn/connectors/google-sheets-connector.md): Connect Mammoth to Google Sheets with a Google sign-in, choose a spreadsheet and tab, and import it as a dataset that refreshes manually or on a schedule. - [Apache Hadoop connector](https://mammoth.io/docs/learn/connectors/hadoop-connector.md): Connect Mammoth to Apache Hadoop through HiveServer2: required access, how to create a connection, and how to import a Hive table or HiveQL query. - [Hubspot connector](https://mammoth.io/docs/learn/connectors/hubspot-connector.md): Connect Mammoth to HubSpot with OAuth, query CRM tables with SQL or plain-language AI query generation, and save the results as a dataset. - [Connectors](https://mammoth.io/docs/learn/connectors.md): Connect Mammoth to databases, warehouses, cloud storage, CRM, marketing, e-commerce, accounting tools, and REST APIs, and import the data as datasets. - [Mailchimp connector](https://mammoth.io/docs/learn/connectors/mailchimp-connector.md): Connect Mammoth to Mailchimp with OAuth, query audience and campaign tables with SQL, and save the results as a dataset. - [Microsoft Dynamics connector](https://mammoth.io/docs/learn/connectors/microsoft-dynamics-connector.md): Connect Mammoth to Microsoft Dynamics 365 with Azure app credentials, query entities with SQL, and save the results as a dataset. - [Monday.com connector](https://mammoth.io/docs/learn/connectors/monday-connector.md): Connect Mammoth to monday.com with a personal access token, query board tables with SQL, and save the results as a dataset. - [MongoDB connector](https://mammoth.io/docs/learn/connectors/mongodb.md): Connect Mammoth to MongoDB: required access, how to create a connection, and how to import a collection or SQL query as a dataset. - [SQL Server connector](https://mammoth.io/docs/learn/connectors/mssql.md): Connect Mammoth to SQL Server: what you need, how to enter credentials, and how to import a table or custom T-SQL query as a dataset. - [MySQL connector](https://mammoth.io/docs/learn/connectors/mysql.md): Connect Mammoth to MySQL: what you need, how to enter credentials, and how to import a table or custom SQL query as a dataset. - [Netsuite connector](https://mammoth.io/docs/learn/connectors/netsuite-connector.md): Connect Mammoth to Oracle NetSuite with OAuth and your account URL, query record tables with SQL, and save the results as a dataset. - [Network requirements & IP whitelisting](https://mammoth.io/docs/learn/connectors/network-requirements.md): Find Mammoth's connecting IP address, default ports, and firewall steps for PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, SFTP, and other connectors. - [OneDrive connector](https://mammoth.io/docs/learn/connectors/onedrive-connector.md): Connect Mammoth to OneDrive with Azure app credentials, select files from a folder tree, import them as datasets, and export a View back to OneDrive. - [Parquet (Azure) connector](https://mammoth.io/docs/learn/connectors/parquet-connector.md): Connect Mammoth to Parquet files in Azure Data Lake Storage with an access key, then query a file or folder and save it as a dataset. - [PDF Extraction](https://mammoth.io/docs/learn/connectors/pdf-extraction.md): Extract tables and text from PDFs and images in the Data Library with AI, review the detected tables, and save them as datasets. - [PostgreSQL connector](https://mammoth.io/docs/learn/connectors/postgresql-connector.md): Connect Mammoth to PostgreSQL: what you need, how to enter credentials, and how to import a table or custom SQL query as a dataset. - [REST API](https://mammoth.io/docs/learn/connectors/rest-api.md): Connect Mammoth to any HTTP or HTTPS JSON endpoint with the REST API connector: authenticate, set a data root path, paginate, test, and save a dataset. - [Salesforce connector](https://mammoth.io/docs/learn/connectors/salesforce-connector.md): Connect Mammoth to Salesforce with OAuth for production, sandbox, or custom-domain orgs, query objects with SQL, and save the results as a dataset. - [SAP HANA connector](https://mammoth.io/docs/learn/connectors/sap-hana-connector.md): Connect Mammoth to SAP HANA: required credentials, port guidance, how to create a connection, and how to import a table or SQL query as a dataset. - [SendGrid connector](https://mammoth.io/docs/learn/connectors/sendgrid-connector.md): Connect Mammoth to SendGrid with a restricted API key, query email delivery tables with SQL, and save the results as a dataset. - [SFTP connector](https://mammoth.io/docs/learn/connectors/sftp.md): Connect Mammoth to an SFTP server using a password, a Mammoth-generated SSH key, or your own private key, then import a file and set up refreshes. - [SharePoint connector](https://mammoth.io/docs/learn/connectors/sharepoint-connector.md): Connect Mammoth to SharePoint Online with Azure app credentials, browse a document library, and import a file as a dataset. - [Shopify connector](https://mammoth.io/docs/learn/connectors/shopify-connector.md): Connect Mammoth to Shopify with OAuth and your .myshopify.com store URL, query store tables with SQL, and save the results as a dataset. - [Snowflake connector](https://mammoth.io/docs/learn/connectors/snowflake-connector.md): Connect Mammoth to Snowflake: required credentials and permissions, how to create a connection, and how to import a table or SQL query as a dataset. - [Understanding data preview & query](https://mammoth.io/docs/learn/connectors/understanding-data-preview-query.md): Use the connector query and preview screen: pick a table, write or generate a SQL query, read the preview, and save a dataset with an update method. - [Xero connector](https://mammoth.io/docs/learn/connectors/xero-connector.md): Connect Mammoth to Xero with OAuth, pick an organisation, query accounting tables with SQL or AI query generation, and save the results as a dataset. - [Creating dashboards](https://mammoth.io/docs/learn/dashboard/creating-dashboards.md): Create a Mammoth dashboard: pick your data or a template, choose a format, describe what you want to the AI, then refine it in the editor and save. - [Dashboard filters](https://mammoth.io/docs/learn/dashboard/dashboard-filters.md): Dashboard filter controls in Mammoth (dropdown, chips, checklist, search, range, motion), how to add and scope them, and how they affect charts. - [Multi-page navigation](https://mammoth.io/docs/learn/dashboard/dashboard-navigations.md): Split a Mammoth dashboard into pages with the Add menu, the Manage pages panel, or the AI chat, and choose top tabs or a left rail for navigation. - [Dashboard themes](https://mammoth.io/docs/learn/dashboard/dashboard-themes.md): The Mammoth dashboard styles (Stone, Modern, Editorial and more), light and dark mode, custom workspace styles, shape controls, and the signature. - [Dashboards](https://mammoth.io/docs/learn/dashboard.md): Overview of Mammoth Dashboards: AI-built dashboards, docs, slides and Q&A pages on your data, with filters, pages, styles, and sharing by link or to signed-in users. - [Managing dashboards](https://mammoth.io/docs/learn/dashboard/managing-dashboard.md): Find, edit, duplicate, archive, and delete Mammoth dashboards; keep them in sync with new data; see who views them in Audience; download a PDF. - [Publishing and sharing dashboards](https://mammoth.io/docs/learn/dashboard/share-dashboard.md): Publish a Mammoth dashboard, choose Sign-in required or Anyone with the link, invite viewers by email, and unpublish. Covers the Share panel. - [Add or replace data](https://mammoth.io/docs/learn/data-library/add-or-replace-data.md): Append new rows to a Mammoth dataset or replace it, using a file upload or another dataset. Covers update methods, column mapping, validation, and results. - [Cloning and moving datasets](https://mammoth.io/docs/learn/data-library/clone-move-data.md): Clone a Mammoth dataset into an independent copy, including into another project through the API, or move datasets and folders within a project. Covers clone options, permissions, and known limitations. - [The dataset panel](https://mammoth.io/docs/learn/data-library/dataset-panel.md): Reference for the Mammoth dataset panel: header actions, Overview with Views and preview, Settings and schema, Batches, and Automations sections. - [Data Library](https://mammoth.io/docs/learn/data-library.md): The Data Library holds a project's datasets plus reusable Parameters and Snippets. Find guides to the dataset panel, adding data, cloning, and moving. - [Parameters and snippets](https://mammoth.io/docs/learn/data-library/parameters-snippets.md): Define reusable values (Parameters) and code blocks (Snippets) once and reference them across Mammoth pipelines. Covers creating, editing, and scope. - [Batch Management](https://mammoth.io/docs/learn/data-sources/batch-management.md): Master dataset versions with combine vs replace modes, batch identification, and automated historical tracking. - [Batch Operations in Detail](https://mammoth.io/docs/learn/data-sources/batch-operations-detail.md): Advanced guide to Mammoth's batch management system for versioning, historical data, and multi-source consolidation. - [Dataset Creation Options](https://mammoth.io/docs/learn/data-sources/dataset-creation-options.md): Compare the dataset creation methods to choose the optimal approach for your data workflow - [File Upload](https://mammoth.io/docs/learn/data-sources/file-upload.md): Upload CSV, Excel, text, XML, archive, PDF, and image files from your computer to create datasets, with automatic schema detection. - [Generate Dataset with AI](https://mammoth.io/docs/learn/data-sources/generate-with-ai.md): Describe a dataset in plain language and Mammoth generates up to 500 rows of realistic test data for pipelines, dashboards, and training. - [Import from URL](https://mammoth.io/docs/learn/data-sources/import-from-url.md): Create a dataset from a file at a web or cloud storage URL, including signed S3 URLs and Dropbox links, without downloading and re-uploading it. - [Data Sources](https://mammoth.io/docs/learn/data-sources.md): Understand the methods to bring data into Mammoth Analytics and choose the right approach for your workflow. - [Paste from Clipboard](https://mammoth.io/docs/learn/data-sources/paste-from-clipboard.md): Create a dataset by pasting cells copied from Excel, Google Sheets, or another spreadsheet, and learn what Mammoth detects and where paste falls short. - [Sample Files](https://mammoth.io/docs/learn/data-sources/sample-files.md): Browse and load built-in sample datasets across business, technology, finance, and healthcare to try Mammoth without uploading your own data. - [Supported File Formats](https://mammoth.io/docs/learn/data-sources/supported-formats.md): Complete guide to all file formats you can upload to Mammoth, including size limits and format-specific best practices. - [View Management](https://mammoth.io/docs/learn/data-sources/view-management.md): Master Views, Batches, and version control to organize data transformations and collaborate effectively with your team. - [Webhooks](https://mammoth.io/docs/learn/data-sources/webhooks.md): Create a webhook dataset so external systems can push JSON or form data to a Mammoth URL, with Combine or Replace mode and an optional secure header. - [Column Browser](https://mammoth.io/docs/learn/explore-discover/column-browser.md): Use the Mammoth Column Browser to search, hide, show and explore columns in a View, and the Column Manager to rename, retype, reorder or remove them. - [Data Quality Report](https://mammoth.io/docs/learn/explore-discover/data-quality-report.md): The Data Quality Report scores a View against six DAMA dimensions, lists critical issues and warnings, and recommends pipeline tasks to fix them. - [Date column cards](https://mammoth.io/docs/learn/explore-discover/explore-cards-date.md): Date Explore Cards chart a date column by day, month or year, with Earliest and Latest values, so you can spot seasonality, gaps and future dates. - [Explore Cards—interactions](https://mammoth.io/docs/learn/explore-discover/explore-cards-interactions.md): How to select, filter and exclude values in Explore Cards, read the filter badge, and convert explore filters into a new View or a Conditional Filter task. - [Numeric column cards](https://mammoth.io/docs/learn/explore-discover/explore-cards-numeric.md): Numeric Explore Cards show a column's histogram, five summary statistics and automatic range grouping so you can spot outliers and placeholder values. - [Explore Cards](https://mammoth.io/docs/learn/explore-discover/explore-cards-overview.md): Explore Cards show the values, distribution or timeline of a column above the Mammoth data grid, and let you filter the grid by clicking a value. - [Text column cards](https://mammoth.io/docs/learn/explore-discover/explore-cards-text.md): Text Explore Cards list a text column's distinct values with counts so you can spot spelling, case and whitespace problems, then filter or fix them. - [Explore & discover](https://mammoth.io/docs/learn/explore-discover.md): Explore a Mammoth View before you transform it: Explore Cards, the Metrics panel, the Data Quality Report and View Insights, and which one to start with. - [Metrics panel](https://mammoth.io/docs/learn/explore-discover/metrics-panel.md): Create single-value metrics such as Sum, Average or custom formulas in the Mammoth Metrics panel, format them, and lock them while explore filters change. - [View Insights](https://mammoth.io/docs/learn/explore-discover/view-insights.md): View Insights uses AI to turn your View's column statistics into 3-7 plain-language observations. Learn how to generate them, when to regenerate, and limits. - [When to use each discovery tool](https://mammoth.io/docs/learn/explore-discover/when-to-use-each.md): Choose between Explore Cards, View Insights, the Data Quality Report and the Metrics panel based on your goal, with workflows for combining them. - [Download options](https://mammoth.io/docs/learn/export-share/download-options.md): Download a Mammoth View as CSV or XLSX, or save it as a new Mammoth Dataset; includes the 50,000-row XLSX limit and download troubleshooting. - [Export formats](https://mammoth.io/docs/learn/export-share/export-formats.md): Compare the CSV, XLSX, and Mammoth Dataset options in the Download dialog, including the 50,000-row XLSX limit. - [Export & share](https://mammoth.io/docs/learn/export-share.md): Overview of exporting and sharing Mammoth data: Download files, Export to destinations and Live links, and Publish as a dashboard. - [Live links](https://mammoth.io/docs/learn/export-share/live-links.md): Create a Live Link, a persistent CSV URL that serves a View's latest data after each pipeline run, and connect it to Tableau, Power BI, or Excel. - [Publish](https://mammoth.io/docs/learn/export-share/published-views.md): Use the Publish button to open the create page and build a dashboard, presentation, document or Q&A from a View, then share it and control how it updates. - [Send to destinations](https://mammoth.io/docs/learn/export-share/send-to-destinations.md): Send a View to Mammoth managed or external destinations (databases, SFTP, SharePoint, Tableau, REST API) after every pipeline run, with Auto-sync controls. - [FAQ and common tasks](https://mammoth.io/docs/learn/faq.md): Quick answers to common Mammoth how-to questions on data import, transforms, automations, sharing, dashboards, access, and recovery, each linked to the full guide. - [Getting started as a data analyst](https://mammoth.io/docs/learn/getting-started/as-a-data-analyst.md): A 30-day plan for data analysts moving from manual Excel workflows to automated Mammoth pipelines, with the five core transformations to learn first. - [Getting started as a manager](https://mammoth.io/docs/learn/getting-started/as-a-manager.md): For data team managers: set up a Mammoth project, invite your team, standardize recurring workflows, review the Activity Log, and share dashboards. - [Getting started as a technical user](https://mammoth.io/docs/learn/getting-started/as-a-technical-user.md): For technical users: validate SQL and large-data support, build reusable pipeline templates, and use window functions and automations to enable your team. - [Core concepts](https://mammoth.io/docs/learn/getting-started/core-concepts.md): Definitions of the seven core Mammoth concepts: Workspace, Project, Dataset, Batch, View, Pipeline, and Task, and how they nest and work together. - [Your first pipeline](https://mammoth.io/docs/learn/getting-started/first-pipeline.md): Build your first data transformation pipeline in Mammoth with step-by-step guidance from data upload to automated workflow. - [Getting started with Mammoth](https://mammoth.io/docs/learn/getting-started.md): 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. - [Key features overview](https://mammoth.io/docs/learn/getting-started/key-features.md): Overview of Mammoth's seven core capabilities: pipelines, AI transformations, Explore Cards, Live Connections, AI dashboards, Automations, and export. - [Quick Start Guide](https://mammoth.io/docs/learn/getting-started/quick-start.md): Build your first Mammoth pipeline with this hands-on tutorial: upload, explore, transform, and export a messy sales file. - [First-time workspace setup](https://mammoth.io/docs/learn/getting-started/setup-workspace-guide.md): Set up a Mammoth workspace: configure general settings, invite users with roles, create projects, and generate API tokens before building pipelines. - [What is Mammoth Analytics?](https://mammoth.io/docs/learn/getting-started/what-is-mammoth.md): Mammoth Analytics is a no-code data preparation and AI dashboard platform. Learn what it does, who it is for, and what it does not do. - [Glossary](https://mammoth.io/docs/learn/glossary.md): Definitions of Mammoth terms such as Workspace, Project, Dataset, Batch, View, Pipeline, Automation, Data Check, and Checkpoint, each linked to its main docs page. - [Learn](https://mammoth.io/docs/learn.md): Index of the Mammoth Learn section, with guides for getting started, data, pipelines, dashboards, automations, administration, security, and performance. - [Limits and quotas](https://mammoth.io/docs/learn/limits-and-quotas.md): Reference for Mammoth limits and quotas, including file upload size, row limits, AI limits, schedule intervals, name lengths, API rate limits, and per-plan allowances. - [Activity Log](https://mammoth.io/docs/learn/monitor/activity-log.md): Reference for the Mammoth Activity Log: what it records, project and workspace scope, filters, investigation steps, and export to CSV, XLSX, or a dataset. - [Monitor](https://mammoth.io/docs/learn/monitor.md): Use Monitor to see what needs attention, what is running, and what has auto-sync paused in a Mammoth project, plus the Activity Log of workspace events. - [Connector performance and best practices](https://mammoth.io/docs/learn/performance/connector-optimization.md): Make Live Connection refreshes faster and more reliable: select only needed columns, pull incrementally, schedule off-peak, and troubleshoot timeouts. - [Performance](https://mammoth.io/docs/learn/performance.md): Speed up Mammoth pipelines and Live Connection refreshes: filter early, use Draft mode, prefer Lookup over Join, and check storage and upload limits. - [Pipeline optimization](https://mammoth.io/docs/learn/performance/pipeline-optimization.md): Speed up Mammoth pipelines: filter early, order tasks from light to heavy, remove unused columns, avoid join fan-out, and use Draft mode. - [Creating a project](https://mammoth.io/docs/learn/projects/create-project.md): Create a Mammoth project, set its name, color, date handling, and access level, then leave or delete it. Includes who can delete a project. - [Projects](https://mammoth.io/docs/learn/projects.md): Projects group datasets and automations in a Mammoth workspace and control who can access them. Learn access levels, project roles, and the core project tasks. - [Project roles and permissions](https://mammoth.io/docs/learn/projects/project-roles-and-permissions.md): Compare what Project Admins and Project Editors can do in a Mammoth project, how roles are assigned, and how workspace roles relate to project roles. - [Project settings](https://mammoth.io/docs/learn/projects/project-settings.md): Change a Mammoth project's name, color, date handling, and who can access it. Project Admins only. Includes the settings reference and FAQs. - [Sharing a project](https://mammoth.io/docs/learn/projects/project-sharing.md): Add workspace users or invite new people to a Mammoth project, change project roles, remove users, and leave a project from the Share Project modal. - [Access control](https://mammoth.io/docs/learn/security-overview/access-control.md): The complete roles and permissions matrix for Mammoth workspace, project, and dashboard roles, plus how access control is enforced. - [Audit logs](https://mammoth.io/docs/learn/security-overview/audit-logs.md): Use the Mammoth Activity Log as an audit trail: what it records, how to filter by time, event, category, and author, and how to export it. - [Sign-in and authentication](https://mammoth.io/docs/learn/security-overview/authentication.md): Sign-in methods for Mammoth: email and password, enterprise SSO, two-factor authentication, session security, and API tokens for integrations. - [Compliance](https://mammoth.io/docs/learn/security-overview/compliance.md): Mammoth's SOC 2 Type II and ISO 27001 certifications, GDPR and HIPAA support, and how the Activity Log supports your own audit and reporting. - [Data security](https://mammoth.io/docs/learn/security-overview/data-security.md): How Mammoth encrypts connector credentials, protects browser sessions, where customer data is stored, and which network ports data sources must allow. - [Security and compliance overview](https://mammoth.io/docs/learn/security-overview.md): How Mammoth secures your data: sign-in methods, role-based access control, credential encryption, the Activity Log, data location, and compliance. - [AI SQL Query](https://mammoth.io/docs/learn/transform/ai-sql-query.md): Use AI SQL Query in Mammoth to turn a plain-English request into a SQL query on your View, review or edit the SQL, and apply it as a pipeline task. - [Column Management](https://mammoth.io/docs/learn/transform/column-management.md): Reference for the Column Management functions in Mammoth: add, copy, combine, convert, remove and split columns, with settings for each. - [Column menu](https://mammoth.io/docs/learn/transform/column-menu.md): Reference for the Mammoth Column Menu: format, sort, freeze, rename, hide or remove a column, and which actions are Display Changes or pipeline tasks. - [Conditional Highlights](https://mammoth.io/docs/learn/transform/conditional-highlights.md): Use Conditional Highlights in Mammoth to color cells or rows with Condition Rules and Color Scales, manage rule order, and preview matches with focus mode. - [Pipeline Controls](https://mammoth.io/docs/learn/transform/controls.md): Add Data Check and Checkpoint controls to a Mammoth pipeline to validate output automatically, send alerts, or pause for human approval before data moves on. - [Date, Numeric & Text](https://mammoth.io/docs/learn/transform/date-numeric-text.md): Reference for the Date, Numeric & Text functions in Mammoth: shift and extract dates, build calculations, rank rows, extract and format text, and parse JSON. - [Filter, Label & Replace](https://mammoth.io/docs/learn/transform/filter-label-replace.md): Reference for the Filter, Label & Replace functions in Mammoth: Conditional Filter, Remove Duplicate Rows, Edit Cells, Fill Missing Values and Bulk Replace. - [Format guide—column type conversions](https://mammoth.io/docs/learn/transform/format-guide.md): Reference for Convert Column Type in Mammoth: date format codes, currency and percentage parsing, Unix timestamp units, and fixes for empty results. - [Function Reference](https://mammoth.io/docs/learn/transform/function-reference.md): Every Mammoth Transform menu function by category, with a one-line description of each and links to the full documentation page for that category. - [Generative AI](https://mammoth.io/docs/learn/transform/generative-ai.md): Use Generative AI in Mammoth to classify, enrich, translate or summarize rows with a plain-English prompt and your own AI provider key, saved as a task. - [Grid Operations](https://mammoth.io/docs/learn/transform/grid-operations.md): Use the multi-column menu in the Mammoth data grid to hide, show or remove several selected columns at once, or run Find & Replace across text columns. - [Transform and pipeline](https://mammoth.io/docs/learn/transform.md): Build a Mammoth pipeline: an ordered list of tasks that clean, reshape and combine data in a View and re-run on every refresh. Includes the Transform menu. - [Pipeline Health and Error State](https://mammoth.io/docs/learn/transform/pipeline-health-and-error-state.md): Why a Mammoth pipeline task fails, how errors appear on task cards, and how to fix, delete or work around an errored task and its dependent tasks. - [Rename, format, and sort columns](https://mammoth.io/docs/learn/transform/rename-format-and-sort-columns.md): Rename, format, sort, hide, freeze and reorder columns in a Mammoth View. These are Display Changes, which never alter data or add pipeline tasks. - [Reshape, Group & Pivot](https://mammoth.io/docs/learn/transform/reshape-group-pivot.md): Reference for Group & Pivot and Unpivot in Mammoth: summarize rows by group, pivot values into columns, or convert wide data into long format. - [Understanding the pipeline](https://mammoth.io/docs/learn/transform/understanding-the-pipeline.md): How the Mammoth Pipeline panel works. Add, edit, suspend, reorder, copy and delete tasks, and use Draft mode to apply several changes at once. - [Unify](https://mammoth.io/docs/learn/transform/unify.md): Reference for the Unify functions in Mammoth: Join and Lookup to combine data from another View, and Send to Dataset to write pipeline output to a dataset. - [Trash and recovery](https://mammoth.io/docs/learn/trash-recovery.md): Deleted Datasets, Views, Automations, Dashboards, and Folders stay in Trash for the trash retention period. See what is recoverable, how to restore or purge items, and what is not. - [Connection troubleshooting](https://mammoth.io/docs/learn/troubleshooting/connection-troubleshooting.md): Diagnose Live Connection failures: import and export authentication errors, pipeline errors, schema mismatches, firewall and network issues, and rate limits. - [Dashboard issues](https://mammoth.io/docs/learn/troubleshooting/dashboard-issues.md): Fix Mammoth dashboard problems: blank screens, stale data, failed AI generation, publishing limits, broken share links, layout glitches, and slow loading. - [Data quality issues](https://mammoth.io/docs/learn/troubleshooting/data-quality-issues.md): Read the Mammoth Data Quality score and its six dimensions, and fix duplicates, missing values, type mismatches, outliers, and inconsistent formatting. - [Error messages reference](https://mammoth.io/docs/learn/troubleshooting/errors.md): Reference of common Mammoth errors and HTTP status codes (401, 403, 429), grouped by area, with the cause and fix for each. - [Troubleshooting](https://mammoth.io/docs/learn/troubleshooting.md): Find the right Mammoth troubleshooting guide by symptom, run a quick diagnostic checklist, learn what the Activity Log records, and contact support. - [Performance issues](https://mammoth.io/docs/learn/troubleshooting/performance-issues.md): Diagnose slow Mammoth pipelines, dashboards, exports, and scheduled automations, and fix large-dataset and over-broad schedule problems. - [Transformation errors](https://mammoth.io/docs/learn/troubleshooting/transformation-errors.md): Fix Mammoth pipeline transformation errors: missing columns, type mismatches, math expression and join problems, and use Data Check gates to prevent them. - [Workspace management](https://mammoth.io/docs/learn/workspace-management.md): Create and configure a Mammoth workspace: rename it, invite users, manage plans and storage, and set up API access. Start here for Workspace Owners. - [Creating a workspace](https://mammoth.io/docs/learn/workspace-management/workspace-creation.md): Create a new Mammoth workspace from the workspace dropdown, choose a plan, and see what a new workspace includes. Also covers renaming and deleting. - [Workspace settings](https://mammoth.io/docs/learn/workspace-management/workspace-settings.md): Reference for Mammoth Workspace Settings: rename the workspace, invite and manage users, manage plan and storage, and create API keys and external keys. - [Migrating from Alteryx to Mammoth](https://mammoth.io/docs/migration/from-alteryx.md): Compare Alteryx and Mammoth by audience, features and automation, then choose full migration, a hybrid setup or an evaluation period. - [Migrating from Excel to Mammoth](https://mammoth.io/docs/migration/from-excel.md): Move Excel workflows to Mammoth pipelines: when to migrate, how formulas and pivots translate, and a four-phase process with parallel validation. - [Migrating from Power Query](https://mammoth.io/docs/migration/from-power-query.md): Decide whether Mammoth complements or replaces Power Query: comparison, hybrid architecture with Power BI, step and M code translation, timeline. - [Python/pandas comparison](https://mammoth.io/docs/migration/from-python-pandas.md): Compare Mammoth pipelines with Python pandas for loading, filtering, joins and aggregations, with a worked example and hybrid patterns using the SDK. - [Migrating from Tableau Prep to Mammoth](https://mammoth.io/docs/migration/from-tableau-prep.md): Compare Tableau Prep and Mammoth, choose a complement or replace strategy, and follow a phased plan to rebuild flows as pipelines and dashboards. - [Migration](https://mammoth.io/docs/migration.md): Guides for moving data workflows from Excel, Tableau Prep, Power Query, Alteryx or Python and pandas to Mammoth, plus a seven-phase migration checklist. - [Migration Checklist](https://mammoth.io/docs/migration/migration-checklist.md): A seven-phase project plan for moving workflows from Excel, Alteryx or Python to Mammoth, with roles, validation steps, training and checklists.