Connect Givebutter and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
New Campaign
New Transaction
Campaign Updated
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Givebutter and Neo4j
Enable Integrations or automations with these events of Givebutter and Neo4j
Triggers
New Campaign
Triggers when a campaign is created
New Transaction
Triggers when a new transaction is created
Campaign Updated
Triggers when a campaign is updated
Actions
Create a Campaign
Create a Campaign
Create a Contact
Create a Contact.
Create a Transaction
Create a Transaction
Update a Campaign
Update an existing Campaign.
Update Contact
Update an existing Contact.
Create a Fund
Create a new Fund.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your Givebutter and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Givebutter triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Givebutter 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 Givebutter 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 Givebutter 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 Givebutter
Givebutter is a fundraising platform that helps you raise money online with donation forms, team fundraising campaigns, and ticketing.
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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