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Building Actions

Building Actions

Actions are automated data pipelines that run based on triggers. This guide walks you through creating an action from start to finish, including configuration, testing, and deployment.

Action Anatomy​

Every action has these components:

[Trigger] → [Data Sources] → [Transformations] → [Actions]
  • Trigger - What starts the action (schedule, HTTP, manual)
  • Data Sources - Where data comes from (entities, views, other actions)
  • Transformations - How data is processed (filter, transform, merge)
  • Actions - What happens with results (write, API call, response)

Creating a New Action​

Step 1: Open the Designer​

  1. Click Actions in the sidebar
  2. Click + New in the toolbar
  3. Select Action

A new canvas opens with an empty action.

Step 2: Add a Trigger​

Every action needs at least one trigger. Click Action in the toolbar or drag from the Element Panel:

Schedule Trigger

  • Runs on a time schedule (cron)
  • Good for: Regular syncs, batch processing, reports

HTTP Trigger

  • Creates a REST API endpoint
  • Good for: Real-time requests, webhooks, integrations

Manual Trigger

  • Runs on-demand
  • Good for: Testing, ad-hoc processing, migrations

From Action Trigger

  • Called by other actions
  • Good for: Reusable logic, modular design

Step 3: Add Data Sources​

Add entities to pull data into your action:

  1. Click Entity in the toolbar (or drag from panel)
  2. Select your connection
  3. Choose the table/endpoint
  4. Click Add

Tip: Select multiple entities at once by holding Cmd/Ctrl while clicking.

Step 4: Connect Nodes​

Draw connections to establish data flow:

  1. Click the output handle (right side) of a node
  2. Drag to the input handle (left side) of the next node
  3. Release to create the connection

Data flows left to right through your connections.

Step 5: Add Transformations​

Process your data with transform nodes:

Filter - Keep only rows matching conditions

Status = "Active" AND Amount > 100

Transform - Add calculated fields

FullName = FirstName & " " & LastName

Merge - Join data from multiple sources

Orders JOIN Customers ON Orders.CustomerID = Customers.ID

Group By - Aggregate data

GROUP BY Region: SUM(Sales), COUNT(Orders)

Step 6: Add Actions​

Define what happens with the processed data:

Write Data - Save to database or file

Call API - Send data to external service

Response - Return data (for HTTP triggers)

Notification - Send alerts

Step 7: Configure Each Node​

Click each node to open its configuration panel:

  1. Select the node
  2. Panel opens on the right
  3. Configure required settings
  4. Settings auto-save

Step 8: Test with Preview​

Before deploying, test your action:

  1. Select a node in your action
  2. Click Preview (or Cmd/Ctrl + Enter)
  3. View the data at that point
  4. Check for expected results

Preview each node to verify data flows correctly through your pipeline.

Step 9: Save​

Your action auto-saves, but you can manually save:

  1. Click Save (or Cmd/Ctrl + S)
  2. Enter a name if prompted
  3. Choose a category (optional)

Use descriptive names: "Daily Customer Sync" not "Flow1"

Step 10: Deploy​

When ready for production:

  1. Click Deploy in the toolbar
  2. Confirm deployment
  3. Action is now active

For scheduled actions, executions begin at the next scheduled time. For HTTP triggers, the endpoint becomes available.

Example: Daily Sales Sync​

Let's build an action that syncs daily sales data:

1. Trigger​

  • Add Schedule Trigger
  • Set: Daily at 6:00 AM

2. Data Sources​

  • Add "Sales" entity from your database
  • Add "Products" entity

3. Filter​

  • Add Filter node after Sales
  • Condition: OrderDate = TODAY() - 1 (yesterday's orders)

4. Merge​

  • Connect Sales and Products
  • Join on: Sales.ProductID = Products.ID
  • Type: Left Join

5. Transform​

  • Add calculated field: TotalAmount = Price * Quantity
  • Add: Region = COALESCE(CustomerRegion, "Unknown")

6. Group By​

  • Group by: Region, ProductCategory
  • Aggregate: SUM(TotalAmount), COUNT(OrderID)

7. Write Data​

  • Destination: "DailySalesSummary" table
  • Mode: Insert
  • Map fields appropriately

8. Test​

  • Preview at each node
  • Verify data looks correct
  • Check aggregations

9. Deploy​

  • Click Deploy
  • Action runs daily at 6 AM

Action Patterns​

ETL Pattern​

Extract, Transform, Load:

[Entity] → [Filter] → [Transform] → [Write Data]

Fan-Out Pattern​

One source, multiple destinations:

             → [Filter A] → [Write A]
[Entity]
             → [Filter B] → [Write B]

Fan-In Pattern​

Multiple sources, one destination:

[Entity A] →
             [Merge] → [Transform] → [Write]
[Entity B] →

Lookup Pattern​

Enrich data from reference tables:

[Main Data] → [Lookup: Reference Table] → [Transform]

Error Handling Pattern​

Separate success and failure paths:

               → [Success Path] → [Write Success]
[Process]
               → [Error Path] → [Log Errors]

Testing Strategies​

Unit Testing​

Test each node individually:

  • Preview with known input
  • Verify output matches expectations

End-to-End Testing​

Test the complete action:

  • Use Manual trigger first
  • Run with test data
  • Verify final output

Edge Case Testing​

Test unusual scenarios:

  • Empty data sets
  • Null values
  • Large volumes
  • Invalid data

Deployment Checklist​

Before deploying to production:

  • All nodes configured (no red indicators)
  • Connections are healthy
  • Preview shows expected results
  • Error handling in place
  • Appropriate trigger schedule
  • Descriptive name assigned
  • Tested with realistic data

Monitoring After Deployment​

Once deployed:

  1. Check Dashboard - Verify executions appear
  2. Review Insights - Monitor success/failure
  3. Set Alerts - Get notified of failures
  4. Track Performance - Watch execution times

Updating Deployed Actions​

To modify a deployed action:

  1. Open the action in Designer
  2. Make your changes
  3. Test with Preview
  4. Save changes
  5. Changes apply to next execution

No need to redeploy for most changes. Only undeploy if you need to stop executions.

Best Practices​

Design​

  • Keep actions focused (single responsibility)
  • Use views for reusable transformations
  • Document with good names and groups

Testing​

  • Always preview before deploying
  • Test with realistic data volumes
  • Verify edge cases

Monitoring​

  • Check executions regularly
  • Set up failure notifications
  • Review performance trends

Maintenance​

  • Update when source schemas change
  • Review and optimize slow actions
  • Archive unused actions

Troubleshooting​

"Invalid configuration"​

  • Check all required fields are filled
  • Verify connections are healthy
  • Look for red indicators on nodes

"No data returned"​

  • Check source has data
  • Verify filter conditions
  • Confirm date ranges

"Timeout during execution"​

  • Optimize slow queries
  • Add row limits
  • Process in batches

Next Steps​