A workflow in Datamorf is an automated process that defines how data moves, transforms, and gets delivered between your tools and systems, without writing a single line of code. It is the fundamental building block of Datamorf’s automation engine, enabling you to connect applications, apply logic, and synchronize data in real time.

Each workflow follows a structured sequence of steps designed to make complex automations predictable, auditable, and easy to maintain. These steps always follow Datamorf’s standardized ETL (Extract → Transform → Load) model:

1. Trigger – How the Workflow Starts

Every workflow begins with a trigger, which determines when or how it should run.
You can start a workflow in several ways:

• Webhook: A unique URL endpoint receives data from external platforms (e.g., form submissions, CRM updates, or other automation tools).

• Integration Trigger: Automatically start a workflow based on an event in a connected app, for example, “new lead created in HubSpot.”

• Schedule: Run workflows automatically at defined intervals (e.g., every hour, every day, every week).

• Extraction (Reverse ETL): Pull data from external databases or SaaS systems on a recurring schedule and feed it into Datamorf for processing.

2. Data Sources – Gathering Additional Information

Once the workflow starts, it can pull additional data from other systems.
For example, if your trigger only sends an email address, Datamorf can use that value to fetch the contact’s full details from your CRM, enrich company data from a provider like Apollo, or retrieve related records from your database.
You can connect multiple data sources in a single workflow, and use conditions to control which ones are executed.

3. Transformations – Modifying and Enhancing Data

Transformations are where the real power of Datamorf lies. This step allows you to clean, normalize, enrich, or compute new values from your data.
Examples include:

• Standardizing names, phone numbers, or job titles

• Running AI models to generate or interpret text dynamically

• Applying math or string operations

• Executing custom JavaScript code for advanced logic

You can chain transformations together; the output of one can feed into the next; to create sophisticated automation logic without complexity.

4. Destinations – Sending Data Where It Needs to Go

Finally, the processed data is delivered to one or more destinations.
These can include CRMs (e.g., HubSpot, Salesforce), communication tools (e.g., Slack, Gmail), databases, spreadsheets, or even another Datamorf workflow.
You can map computed fields to destination properties, set retry logic, delay execution, or trigger chained workflows for multi-stage automations.

Why Workflows Matter

Workflows let you connect, transform, and act on your data in a single continuous flow, eliminating manual intervention or scattered integrations.
Instead of maintaining multiple scripts or third-party connections, Datamorf workflows provide a centralized, visual, and auditable structure for all your automations.

They are:

• Reusable: One workflow can handle multiple data streams or be triggered from other workflows.

• Scalable: Built to manage millions of executions with automatic scaling.

• Transparent: Each run is logged, allowing full visibility into every transformation and API call.

In short, a Datamorf workflow is a complete automation pipeline that defines how your data should move, evolve, and synchronize across systems, helping teams work faster, more accurately, and with far fewer manual steps.