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Automations, Auto-sync and pipeline governance compared

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

LeverWhat it automatesWhere it livesTrigger
AutomationsRecurring data operations: refresh, consolidate, collect files, scheduled exports, scheduled emailAutomations tab in the Data LibrarySchedule, manual run or file event
Auto-syncRe-running pipeline steps and exports when upstream data changesPer-View and per-export settingsData change
Pipeline governanceData quality gates and human approvals inside a pipelinePipeline task settingsManual approval or data condition
I want to...Use
See what's running right now across my workspaceMonitor in the left sidebar
Keep a database, cloud app or From URL source currentAutomation: Dataset Refresh
Run an export on a fixed schedule, even when the data hasn't changedAutomation: Scheduled Export
Process files my team uploadsAutomation: Data Consolidation
Collect files from external cloud storage into MammothAutomation: File Collection
Send a scheduled email report with data attachedAutomation: Messaging
Re-run pipeline steps when data changesView Auto-sync on each pipeline step
Fire exports when a View updatesExport Auto-sync on each export
Validate data quality before it moves downstreamPipeline governance: Data Check task
Have a person approve data before it moves onPipeline 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, where you can turn it back on. See Control when Views update with 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.

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.

Both appear in the Needs Attention tab of 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.
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