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# Automations, Auto-sync and pipeline governance compared

> When to use a Mammoth automation, View or export Auto-sync, or a Data Check or Checkpoint, with three workflow patterns that combine them end to end.
>
> Applies to: Where: Data Library › Automations; per-View and per-export Auto-sync settings; pipeline task settings

Mammoth automates at three levels, and you can combine them. Automations are project-level scheduling on the **Automations** tab in the Data Library. Auto-sync is View-level and export-level reactivity, and pipeline governance is step-level control.

## Which lever does what

| Lever | What it automates | Where it lives | Trigger |
| --- | --- | --- | --- |
| **Automations** | Recurring data operations: refresh, consolidate, collect files, scheduled exports, scheduled email | **Automations** tab in the Data Library | Schedule, manual run or file event |
| Auto-sync | Re-running pipeline steps and exports when upstream data changes | Per-View and per-export settings | Data change |
| Pipeline governance | Data quality gates and human approvals inside a pipeline | Pipeline task settings | Manual approval or data condition |

| I want to... | Use |
| --- | --- |
| See what's running right now across my workspace | **Monitor** in the left sidebar |
| Keep a database, cloud app or From URL source current | Automation: **Dataset Refresh** |
| Run an export on a fixed schedule, even when the data hasn't changed | Automation: **Scheduled Export** |
| Process files my team uploads | Automation: **Data Consolidation** |
| Collect files from external cloud storage into Mammoth | Automation: **File Collection** |
| Send a scheduled email report with data attached | Automation: **Messaging** |
| Re-run pipeline steps when data changes | View Auto-sync on each pipeline step |
| Fire exports when a View updates | Export Auto-sync on each export |
| Validate data quality before it moves downstream | Pipeline governance: Data Check task |
| Have a person approve data before it moves on | Pipeline governance: Checkpoint task |

## Re-run Views and exports when data changes with Auto-sync

Each pipeline View has an Auto-sync toggle. When it is on, the View re-processes whenever its source data changes: a new file lands, a refresh runs or a manual edit is saved. When it is off, the View "freezes": changes flow up to that point and stop, so you can review data before it moves downstream.

Each export (a connection from a View to a destination such as a database, SFTP or a live link) has its own Auto-sync toggle. On, the export fires whenever its View produces new output. Off, you control when data leaves Mammoth, which suits staging, approval workflows and rate-limited destinations.

Steps and exports with Auto-sync off are listed on the **Paused** tab of [Monitor](https://mammoth.io/docs/learn/automate/monitor-running/#see-what-has-auto-sync-turned-off), where you can turn it back on. See [Control when Views update with Auto-sync](https://mammoth.io/docs/learn/organise/view-auto-sync/).

## Gate data with Data Checks and Checkpoints

A Data Check task tests the data at a pipeline step against conditions you set, such as row counts, column values, null thresholds or custom expressions. If the check fails, it can halt the pipeline at that step and flag the run. See [Remove duplicate rows and add data quality checks](https://mammoth.io/docs/learn/clean-and-shape/duplicates-and-checks/).

A Checkpoint task pauses the pipeline at a step until a designated person approves it, for compliance, staged rollouts or any sign-off. See [Pause a pipeline for approval with a Checkpoint](https://mammoth.io/docs/learn/clean-and-shape/checkpoints/).

Both appear in the **Needs Attention** tab of [Monitor](https://mammoth.io/docs/learn/automate/monitor/) when they need action.

## Common workflow patterns

### Keep cloud datasets fresh and update everything downstream

1. Set up a **Dataset Refresh** automation for each connected dataset.
2. Turn on View Auto-sync on every downstream pipeline View.
3. Turn on Export Auto-sync on exports to external destinations.
4. Watch health in **Monitor** > **Running**.

### Process files as they arrive and notify stakeholders

1. Set up a **Data Consolidation** automation to append files as they're uploaded.
2. Turn on View Auto-sync on the Views that transform the combined data.
3. Set up a **Messaging** automation to send a daily summary email.
4. Add a Data Check task before the final export to catch schema problems.

### Add human review before data ships

1. Build the pipeline with View Auto-sync on for each step.
2. Add a Data Check task where data must meet quality thresholds.
3. Add a Checkpoint task that needs approval before the final export.
4. Turn on Export Auto-sync. The export fires only after the Checkpoint is approved.
5. Reviewers see pending Checkpoints in **Monitor** > **Needs Attention**.

## Related

- [Choose the right automation type](https://mammoth.io/docs/learn/automate/choose-a-type/): compare the five automation types.
- [Control when Views update with Auto-sync](https://mammoth.io/docs/learn/organise/view-auto-sync/): the Auto-sync toggle in full.
- [See running, stuck and paused work](https://mammoth.io/docs/learn/automate/monitor-running/): turn Auto-sync back on.
- [Pause a pipeline for approval with a Checkpoint](https://mammoth.io/docs/learn/clean-and-shape/checkpoints/): set up an approval gate.

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Source: https://mammoth.io/docs/learn/automate/auto-sync-and-governance.md · Updated: 2026-11-08