The Extractor is Datamorf's primary and most important trigger. It performs Reverse ETL — automatically pulling records from your CRM, database, or scraping source on a recurring schedule and feeding them into a workflow for transformation and activation.

This is what makes Datamorf different from event-driven automation tools. Rather than waiting for something new to happen, the Extractor proactively reads your existing records on a schedule — keeping your entire GTM stack in sync with the source of truth, not just reacting to new activity.

Why the Extractor Matters for GTM Teams

• Keeps data clean over time — Regularly re-extracting records lets Datamorf catch stale fields, signals, missing enrichment, duplicates, and outdated properties across existing records — not just when a record is first created.

• Reduces data silos — Data stays synchronized across all GTM tools from a single source, eliminating manual exports and disconnected integrations.

• Works on existing data — Most GTM activation workflows need to process records that already exist in the CRM or database, not only new ones. The Extractor handles this natively.

• Supports scraping — When a CRM or structured API isn't available, the Extractor can pull data from web sources on a schedule.

Setting Up an Extractor

The Extractor configuration panel is organized into three sections:

Settings

Define the basics of how the Extractor connects to its source and when it runs:

• Integration — Select the connected app or database the Extractor should read from (e.g., HubSpot Contacts, a SQL database, a scraping integration).

• Schedule — Set how often the Extractor should run: every hour, every day, once a week, or a custom interval. Datamorf handles all execution automatically — no cron jobs required.

• Webhook (optional) — Optionally expose a webhook endpoint on the Extractor, so it can also be triggered externally in addition to its schedule.

Data

Define exactly what records the Extractor should pull and in what order:

• Filter — Apply conditions to limit which records are extracted. Use the visual condition builder to define rules such as 'Status = Active' or 'Created date > last week'. Filters are optional — without them, the Extractor pulls all available records.

• Sort — Control the order in which records are processed — for example, by creation date descending to process the most recent records first.

• Limit — Set a cap on how many records are fetched per run. Datamorf handles pagination automatically, so large datasets are processed in batches without manual intervention.

• Test — Run a test pull to preview what records would be extracted with your current filter and limit settings. Use this to validate your configuration before activating the workflow.

Throughput

Control how fast and in what format records are delivered into the workflow:

• Delivery speed — By default, records are sent one at a time (1 per second) for stable delivery. You can increase the concurrency level for faster processing of large datasets.

• Batch mode — Enable batch mode to send groups of records together (up to 100 items per batch) instead of one by one. Useful for workflows that process lists rather than individual records.

• Attach metadata — including extraction ID, timestamp, and index — making every record traceable in the run logs.