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, allowing users to interact programmatically with various Dataiku DSS resources. Specifically for the Dataset resource and the Get Data - Alternative Version operation, it streams the content of a dataset's alternative version from a specified project in Dataiku DSS.
This operation is useful when you want to retrieve dataset data that may represent an alternative or specific version different from the default dataset view. It can be used in scenarios such as:
- Extracting historical or experimental versions of datasets.
- Accessing snapshot data for auditing or comparison.
- Feeding alternative dataset versions into downstream workflows or analyses.
Example use case: You have multiple versions of a dataset representing different stages of data processing, and you want to fetch a particular version to validate or analyze it without affecting the main dataset.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique key identifying the Dataiku DSS project containing the dataset. |
| Dataset Name | The name of the dataset within the specified project whose alternative version data is fetched. |
| Request Body | A JSON object representing additional request parameters or filters to customize the data retrieval. |
Output
The output contains the streamed content of the dataset's alternative version. The data is returned in the json field of the output items.
- If the response is JSON, it will be parsed and returned as structured JSON.
- If the response is binary (e.g., file content), it will be prepared as binary data under the
binary.dataproperty with an appropriate filename extension. - For this operation, the typical output is JSON data representing the dataset content.
Dependencies
- Requires a valid connection to a Dataiku DSS instance.
- Requires an API key credential for authentication with the Dataiku DSS API.
- The node expects the Dataiku DSS server URL and user API key to be configured in the credentials.
- No other external dependencies are required.
Troubleshooting
- Missing Credentials Error: If the API key or server URL is not provided, the node throws an error indicating missing credentials. Ensure the API key credential is properly set up.
- Required Parameters Missing: The node validates required parameters like Project Key and Dataset Name. Omitting these will cause errors. Provide all mandatory fields.
- API Errors: If the Dataiku DSS API returns an error (e.g., unauthorized, not found), the node surfaces the error message. Check your permissions and parameter correctness.
- Parsing Errors: If the response is expected to be JSON but cannot be parsed, the raw text is returned instead. Verify the API endpoint and request body.
- Binary Data Handling: For operations returning files, ensure the binary data is handled correctly downstream.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Datasets API Reference
- n8n Documentation on Creating Custom Nodes
This summary focuses on the Dataset resource and the "Get Data - Alternative Version" operation as requested.