Connect Billetweb and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
Recent Attendee Check-ins
Attendee Created or Updated
New Event Created
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Billetweb and Neo4j
Enable Integrations or automations with these events of Billetweb and Neo4j
Triggers
Recent Attendee Check-ins
Returns attendees for the selected event who were checked in within the recent time window.
Attendee Created or Updated
Triggers when an attendee is created or updated for a selected event (or all events). Returns attendee and order details and supports filtering by tag and by creation/update.
New Event Created
Detects newly created events on Billetweb and returns details for events added since the last check.
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Frequently Asked Questions
To start, connect both your Billetweb and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Billetweb triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Billetweb 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 Billetweb 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 Billetweb 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 Billetweb
Billetweb is a comprehensive platform for event management and ticketing, offering tools to organize, promote, and manage events efficiently.
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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