Connect Everhour and Neo4j to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Everhour
Choose an Action
Automatically do this!
- Request a new Action for Neo4j
Enable Integrations or automations with these events of Everhour and Neo4j
Enable Integrations or automations with these events of Everhour and Neo4j
Actions
Create Task
Creates a new task in Everhour.
Create Project
Creates a new project.
Add Time
Adds your time to a task.
Create Section
Сreates section (column) in a project.
Start Timer
Starts a new timer for a task.
Stop Timer
Stops the current timer.
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Frequently Asked Questions
To start, connect both your Everhour and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Everhour triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Everhour 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 Everhour 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 Everhour 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 Everhour
Everhour is a time tracking and project management tool designed to help teams and businesses efficiently manage their time and resources. It integrates seamlessly with popular project management platforms, allowing users to track time, set budgets, and generate insightful reports to optimize productivity and project outcomes.
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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