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Triggers

Trigger Types define how and when a Matia integration begins a sync. They allow you to control execution timing so your data pipelines run exactly when your business needs them to whether that means running only on demand, on a predictable schedule, or automatically in response to upstream events. The trigger type is selected when creating an integration and can be modified at any time from the Integration Settings tab. Matia offers three types of triggers for ETL integrations: Manual, Interval, and Cron.
  • Choosing a Manual trigger means the integration will never run on its own. Instead, a sync begins only when initiated by a user through the Sync Now button.
  • The Interval trigger allows you to define how frequently the sync should run. You can choose from a range of preset frequencies ranging from every 5 minutes to 30 days. Interval triggers also require a base time, which determines when the first sync should run and serves as the anchor point for all future intervals.
  • With the Cron trigger, you can define highly customized sync schedules by entering a cron expression. This option supports complex schedules that go beyond simple intervals - for example, running a sync every weekday at 8 AM, on the first business day of each month, or at irregular hours throughout the day.
For Reverse ETL integrations, Matia supports all three trigger types above, but also adds an additional option: dbt trigger. When enabled, Matia monitors a selected dbt Cloud job and initiates the integration the moment that job completes successfully.

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Sync Modes

Sync Modes determine how Matia extracts and delivers data from your source system to your destination. You can configure the sync mode of a specific stream (table) in the Integration Schema tab. The available sync modes depend on the specific connector and the capabilities of the underlying source and destination systems. ETL integrations support several sync modes at the stream (table) level. Each mode controls how Matia identifies new or updated records and how much data is retrieved during each sync.
  • Full Refresh: Matia reads all records from the source and writes them to the destination during every sync.
  • Incremental: Matia retrieves only records that have been added or modified since the last sync.
  • Append-Only: Matia pulls only new records, without modifying previously synced ones.
Reverse ETL integrations use integration-level sync modes that define how records are written to operational tools or SaaS platforms.
  • Upsert: Matia creates new records when they don’t exist in the destination and updates existing ones when they do.
  • Update Only: Matia updates existing records but does not create new ones.
  • Append Only: Matia creates new records for every synced row and never updates previously created ones.
  • Full Refresh: Matia clears the destination dataset and re-inserts all records every time the sync runs.

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Schema Tab

In Matia, the Integration Schema tab lets you customize your integration behavior. You can:
  • Configure different sync modes for individual streams
  • Enable or disable specific streams so you can move only the data you need
  • Manually define cursor fields and unique keys that will be used to detect newly created records in the source
Note: Some sources and streams support only Full Refresh and do not allow Incremental Sync. Refer to the Connector’s documentation for details.
If a stream has dependencies (child streams), configuration changes such as enabling/disabling the stream or selecting a sync mode must be applied at the parent stream. Updates to the parent are automatically reflected in its dependent child streams. For each stream in the schema, you can inspect its fields, view field types (such as date, boolean, or string), disable specific fields, or mark fields to be hashed as needed. These field level actions remain fully available regardless of stream hierarchy.

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Schema Changes

Integration schema changes refer to any updates to the source schema.modifications in the structure of your data as defined in an integration’s schema. This can include addedadding or removedremoving streams (tables), added or removedchanging fields (columns) names or changes in field data types, or updating the relationships between streams. Matia automatically detects and surfaces these changes, helping you stay informed and ensuring your integrations remain accurate and reliable as your source data evolves. You can configure how your integration responds when schema changes occur. Matia supports several behaviors that let you decide how proactively the integration should adopt new structures:
  • Enable All New Tables and Columns: Automatically incorporates all newly detected tables and fields into the integration.
  • Enable Only New Columns: Adds newly introduced fields to existing streams while leaving new tables disabled.
  • Ignore All Schema Changes: Prevents any new tables or fields from being added automatically.

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Post Run Actions

Post Run Actions allow you to automatically perform additional operations immediately after your integration completes a sync. Currently, Matia supports triggering dbt Cloud jobs as a post-run action. This gives you the ability to orchestrate downstream transformations inside your data warehouse without manual intervention, ensuring your data is always fresh, structured, and ready to use. When used as a Post Run Action in Matia, dbt enables you to automatically trigger a specific job as soon as new data arrives from your integration. By linking ingestion and transformation, you can ensure that downstream tables, dashboards, and analytics are always updated with the latest business-ready data. Matia Tag