Connect Codefresh and Neo4j to Build Intelligent Automations
Enable Integrations or automations with these events of Codefresh and Neo4j
Enable Integrations or automations with these events of Codefresh and Neo4j
Triggers
New Project
Triggers when a new project is created in scheduled time.
Actions
Create Project
Creates a new project.
Create Teams
Create a new Codefresh team, add specified users, and apply optional tags.
Create Annotaions
Add an annotation to a Codefresh entity (pipeline, build, or deployment).
Search Project
Search projects with filters.
Delete a Project
Deletes a project.
List Build
Retrieve a paginated list of workflow builds, optionally filtered by status, pipeline, git provider/repo, trigger reason, and date range.
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Frequently Asked Questions
To start, connect both your Codefresh and Neo4j accounts to viaSocket. Once connected, you can set up a workflow where an event in Codefresh triggers actions in Neo4j (or vice versa).
Absolutely. You can customize how Codefresh 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 Codefresh 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 Codefresh 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 Codefresh
Codefresh is a modern DevOps platform that streamlines software development and delivery. It offers powerful CI/CD tools to help developers build, test, and deploy applications efficiently, simplifying complex workflows for teams aiming to enhance their DevOps practices.
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About 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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