From dominodatalab
Connects Domino workloads to external data sources: AWS S3 via Mountpoint CSI, credential propagation with AWS IRSA and Azure Entra ID, and External Data Volumes.
How this skill is triggered — by the user, by Claude, or both
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/dominodatalab:data-connectivityThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides comprehensive knowledge for connecting Domino workloads to external data sources, including AWS S3, Azure storage, and credential propagation.
This skill provides comprehensive knowledge for connecting Domino workloads to external data sources, including AWS S3, Azure storage, and credential propagation.
| Option | Use Case |
|---|---|
| Datasets | Project-level data storage |
| Data Sources | External database connections |
| External Data Volumes (EDV) | Mount external storage as volumes |
| S3 Mountpoint | Direct S3 access as file system |
| Credential Propagation | Pass user identity to cloud services |
| Method | Cloud | Description |
|---|---|---|
| IRSA | AWS | IAM Role for Service Accounts via OIDC |
| Azure Entra ID | Azure | User-based credential propagation |
With Mountpoint S3 configured, access S3 as a local file system:
import pandas as pd
# S3 data appears as local files
df = pd.read_parquet("/mnt/s3-data/datasets/sales.parquet")
With IRSA configured, AWS SDK uses automatic credentials:
import boto3
# No explicit credentials needed - IRSA provides them
s3 = boto3.client('s3')
response = s3.list_objects_v2(Bucket='my-bucket')
EDVs are mounted at configured paths:
# Read from external volume
with open("/mnt/external-data/config.json") as f:
config = json.load(f)
Implementation templates available at: https://github.com/dominodatalab/domino-blueprints
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