Connector service
Connector service gives users in H2O AI Cloud a unified way to connect to external data sources and move data into AI and analytics workflows from a Jupyter notebook, a Wave app, or any Python environment.
Instead of writing bespoke driver code and managing credentials in each application, you connect to a pre-configured Connector in your workspace and stream rows or files directly to your process.
info
For more information about Connectors and related key concepts, see Concepts: Connectors.
User roles
| Role | Responsibilities |
|---|---|
| Platform Administrator | Publishes Connector Templates and Worker Templates. Manages which data source types are available and which workspaces can access them. |
| Workspace Administrator | Creates Connectors and Workers within a workspace by selecting a template and supplying connection details and credentials. |
| Data Scientist / Data Engineer | Opens connections and streams data using the Python client. |
Supported data sources
| Data Source | Category | Notes |
|---|---|---|
| PostgreSQL | Tabular | SQL query and table extraction with column projection and filtering |
| Snowflake | Tabular | SQL query and table extraction; password or key-pair authentication |
| Amazon S3 | Blob | Single object key or prefix download |
| MinIO | Blob | S3-compatible endpoint |
| Google Cloud Storage | Blob | |
| Azure Blob Storage | Blob | |
| HTTP / SFTP / FTP | Blob | |
| BigQuery | Tabular | |
| Delta Lake | Tabular | |
| Apache Hive | Tabular | |
| Confluence | Blob | Pages and attachments from Confluence Cloud |
Note
The data sources available in your environment depend on which Connector Templates your Platform Administrator has published.
Next steps
- Connector Service Python Client — install the Python client and stream your first dataset
- Data Sources — configuration reference for each supported data source
- Secure Credentials — store and reference credentials safely using Secure Store
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