Connect Pointagram and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Pointagram
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Pointagram and Neo4j
Enable Integrations or automations with these events of Pointagram and Neo4j
Actions
Find Players
Looks up a player by matching either player name, player external id or email-address.
Create Player
Creates a new player.
Create or Find Player
Search for an existing player or create a new one if the player is not found.
Add Points
Add points for a player in a score series
Update a Player
Updates an existing player
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Frequently Asked Questions
To start, connect both your Pointagram and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Pointagram triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Pointagram 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 Pointagram 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 Pointagram 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 Pointagram
Pointagram is a platform designed to gamify team activities and enhance engagement through point-based challenges and rewards. It allows teams to create competitions, track progress, and celebrate achievements, fostering a fun and motivating environment.
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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