Connect Coda and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
New Row
Updated Row
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Coda and Neo4j
Enable Integrations or automations with these events of Coda and Neo4j
Triggers
New Row
Triggers when a new row is created
Updated Row
Triggers when a new row is updated within the selected table
Actions
Find a Row
Search for a Coda Row in the selected table using a column match search.
Create a Row
Creates a row in an existing table
Copy a Doc
Creates a copy of the specified Coda doc.
Deletes a doc
deletes a Coda doc.
Update Row
Updates an existing row in the selected table.
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Frequently Asked Questions
To start, connect both your Coda and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Coda triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Coda data is recorded in Neo4j. This includes choosing which data fields go into which fields of Neo4j, setting up custom formats, and filtering out unwanted information.
The data sync between Coda and Neo4j typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Coda and Neo4j. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Coda
Coda is a versatile online tool that combines documents, spreadsheets, and building blocks to create dynamic docs for various needs. It’s used for project management, data tracking, and creating complex documents that function like applications.
Learn MoreAbout Neo4j
Neo4j is a leading graph database platform that enables organizations to unlock the value of connections, influences, and relationships in data. It is designed to handle highly connected data and is used for a variety of applications such as fraud detection, real-time recommendations, and network management.
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