Actions364
- Continuous Activity Actions
- Dataset Actions
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- Compute Metrics
- Create Dataset
- Create Managed Dataset
- Delete Data
- Delete Dataset
- Execute Tables Import
- Get Column Lineage
- Get Data
- Get Data - Alternative Version
- Get Dataset Settings
- Get Full Info
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Dashboard Actions
- Data Collection Actions
- Data Quality Actions
- Compute Rules on Specific Partition
- Create Data Quality Rules Configuration
- Delete Rule
- Get Data Quality Project Current Status
- Get Data Quality Project Timeline
- Get Data Quality Rules Configuration
- Get Dataset Current Status
- Get Dataset Current Status per Partition
- Get Last Outcome on Specific Partition
- Get Last Rule Results
- Get Rule History
- Update Rule Configuration
- DSS Administration Actions
- Job Actions
- Library Actions
- Dataset Statistic Actions
- Discussion Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- LLM Mesh Actions
- Machine Learning - Lab Actions
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Adjust Forecasting Parameters and Algorithm
- Compute Partial Dependencies of Trained Model
- Compute Subpopulation Analysis of Trained Model
- Create ML Task
- Create Visual Analysis
- Create Visual Analysis and ML Task
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get ML Task Settings
- Get ML Task Status
- Get Model Snippet
- Get Partial Dependencies of Trained Model
- Get Scoring Jar of Trained Model
- Get Scoring PMML of Trained Model
- Get Subpopulation Analysis of Trained Model
- Get Trained Model Details
- Get Visual Analysis
- List ML Tasks of Project
- List ML Tasks of Visual Analyses
- List Visual Analyses
- Reguess ML Task
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Compute Subpopulation Analysis of Version
- Create Saved Model
- Delete Version
- Download Model Documentation of Version
- Evaluate MLflow Model Version
- Generate Model Documentation From Custom Template
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get MLflow Model Version Metadata
- Get Partial Dependencies of Version
- Get Saved Model
- Get Subpopulation Analysis of Version
- Get Version Details
- Get Version Scoring Jar
- Set Version User Meta
- Update Saved Model
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Macro Actions
- Plugin Actions
- Download Plugin
- Fetch From Git Remote
- Get File Detail From Plugin
- Get Git Remote Info
- Get Plugin Settings
- Install Plugin From Git
- Install Plugin From Store
- List Files in Plugin
- List Git Branches
- List Plugin Usages
- Move File or Folder in Plugin
- Add Folder to Plugin
- Create Development Plugin
- Create Plugin Code Env
- Delete File From Plugin
- Delete Git Remote Info
- Delete Plugin
- Download File From Plugin
- Move Plugin to Dev Environment
- Pull From Git Remote
- Push to Git Remote
- Rename File or Folder in Plugin
- Reset to Local Head State
- Reset to Remote Head State
- Set Git Remote Info
- Set Plugin Settings
- Update Plugin Code Env
- Update Plugin From Git
- Update Plugin From Store
- Update Plugin From Zip Archive
- Upload File to Plugin
- Upload Plugin
- Project Deployer Actions
- Get Deployment Settings
- Get Deployment Status
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- SQL Query Actions
- Wiki Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Project Actions
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- Streaming Endpoint Actions
- Webapp Actions
- Workspace Actions
Overview
This node integrates with the Dataiku DSS API, enabling users to perform a wide range of operations on various Dataiku DSS resources. Specifically for the Library resource and the Rename File or Folder operation, it allows renaming files or folders within a project's library in Dataiku DSS.
Common scenarios where this node is beneficial include:
- Organizing project libraries by renaming files or folders to follow naming conventions.
- Automating library maintenance tasks such as renaming outdated or incorrectly named files/folders.
- Integrating with workflows that require dynamic renaming of library contents based on external inputs or processing results.
Example use case:
- A data engineering workflow that uploads new scripts or datasets to the library and then renames them according to a timestamped naming scheme for version control.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project containing the library to modify. |
| Request Body | JSON object containing the details of the rename operation, typically specifying the current path and the new name for the file or folder. |
Note: For the Rename File or Folder operation under the Library resource, the key input is the requestBody JSON which should include the necessary parameters for renaming (e.g., source path and target name).
Output
The node outputs the response from the Dataiku DSS API call:
- If the rename operation is successful, the output JSON contains the API's confirmation or updated resource information.
- If the operation involves downloading files or binary content (not typical for rename), the node would output binary data accordingly, but for rename, the output is JSON.
- In case of errors, the node throws an error with details about the failure.
The output is structured as an array of JSON objects, each representing the result of the API call for each input item processed.
Dependencies
- Requires valid Dataiku DSS API credentials, including the server URL and an API key with sufficient permissions to manage the project's library.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API.
- No additional external services are required beyond access to the Dataiku DSS instance.
Troubleshooting
- Missing Credentials Error: The node will throw an error if the Dataiku DSS API credentials are not provided or invalid. Ensure you have configured the API key credential correctly.
- Project Key Required: Many operations, including library modifications, require the
Project Key. Omitting this will cause an error. - Path Required: For renaming files or folders, the request body must specify the current path and the new name; missing these will cause the API call to fail.
- API Errors: If the API returns an error (e.g., permission denied, resource not found), the node will throw an error with the message returned by the API. Check the API response and ensure the user has appropriate permissions.
- Network Issues: Connectivity problems to the Dataiku DSS server will cause request failures. Verify network access and server availability.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Libraries API Reference
- n8n Documentation on Creating Custom Nodes
This summary focuses on the "Library" resource and the "Rename File or Folder" operation as requested.