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 perform a wide range of operations on Dataiku DSS resources. Specifically for the Dataset resource and the Update From Hive Metastore operation, the node updates a dataset in Dataiku DSS by synchronizing it with the corresponding table metadata from the Hive metastore. This is useful when the underlying Hive table schema or properties have changed, and you want the Dataiku dataset to reflect those changes without manually updating it.
Common scenarios include:
- Keeping Dataiku datasets in sync with external Hive tables after schema evolution.
- Automating dataset metadata refreshes as part of data pipeline workflows.
- Ensuring consistency between Hive metastore definitions and Dataiku datasets for accurate downstream processing.
Example: You have a dataset in Dataiku linked to a Hive table. After adding columns or changing types in Hive, you use this node operation to update the Dataiku dataset schema accordingly.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku project containing the dataset. |
| Dataset Name | The name of the dataset within the specified project to update from the Hive metastore. |
Output
The output JSON contains the response from the Dataiku DSS API after performing the update operation. Typically, this will be a confirmation object indicating success or details about the updated dataset.
- The
jsonfield includes the API response parsed as JSON. - No binary data output is expected for this operation.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials (an API key) 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.
- The Dataiku DSS API must be accessible from the environment where n8n runs.
Troubleshooting
- Missing Credentials Error: If the API credentials are not set or invalid, the node throws an error "Missing Dataiku DSS API Credentials". Ensure that the API key credential is properly configured.
- Required Parameter Errors: The node validates required parameters such as Project Key and Dataset Name. Missing these will cause errors like "Project Key is required" or "Dataset Name is required".
- API Request Failures: Network issues, incorrect server URLs, or insufficient permissions can cause API call failures. Check connectivity and API permissions.
- Unexpected Response Format: If the API returns non-JSON or unexpected responses, parsing errors may occur. Review the API server logs or response content.
Links and References
- Dataiku DSS API Documentation
- Hive Metastore Synchronization in Dataiku
- Dataiku DSS Official Website
This summary focuses on the Dataset resource's Update From Hive Metastore operation as requested, based on static analysis of the provided source code and property definitions.