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domino-data-sdk

Use the domino-data Python SDK (dominodatalab-data) for programmatic data access in Domino. Covers DataSourceClient for SQL queries and object storage, DatasetClient for dataset files, TrainingSets for ML data versioning, Feature Store, and VectorDB (Pinecone) integration. Use when querying data sources, downloading datasets, managing training sets, or working with vector databases in Domino.

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Domino Data SDK Skill

This skill provides comprehensive knowledge for working with the domino-data Python SDK (dominodatalab-data) - the official library for Domino's Access Data features.

Installation

# Via pip
pip install -U dominodatalab-data

# Via Poetry
poetry add dominodatalab-data

# In Domino environment (requirements.txt)
dominodatalab-data>=6.0.0

Key Components

ModulePurpose
DataSourceClientQuery SQL databases and access object stores
DatasetClientRead files from Domino Datasets
TrainingSetsVersion and manage ML training data
Feature StoreManage ML features with Git integration
VectorDBPinecone vector database integration

Related Documentation

  • DATA-SOURCES.md - SQL queries and object storage
  • DATASETS.md - Dataset file operations
  • TRAINING-SETS.md - Training data versioning
  • VECTORDB.md - Pinecone integration

Quick Start

Query a Data Source

from domino_data.data_sources import DataSourceClient

# Initialize client (auto-configured in Domino)
client = DataSourceClient()

# Get a data source by name
ds = client.get_datasource("my-redshift-db")

# Execute SQL query
result = ds.query("SELECT * FROM customers WHERE region = 'US'")

# Convert to pandas DataFrame
df = result.to_pandas()

# Or save to parquet
result.to_parquet("output.parquet")

Access Object Storage

from domino_data.data_sources import DataSourceClient

client = DataSourceClient()
ds = client.get_datasource("my-s3-bucket")

# List objects
objects = ds.list_objects(prefix="data/", page_size=100)

# Download a file
ds.download_file("data/input.csv", "local_input.csv")

# Upload a file
ds.put("data/output.csv", open("results.csv", "rb").read())

# Get signed URL
url = ds.get_key_url("data/file.csv", is_read_write=False)

Read from Datasets

from domino_data.datasets import DatasetClient

client = DatasetClient()

# Get dataset by name
dataset = client.get_dataset("training-data")

# List files
files = dataset.list_files(prefix="images/")

# Download file
dataset.download("model.pkl", "local_model.pkl", max_workers=4)

# Get file content as bytes
content = dataset.get("config.json")

Training Sets

from domino_data.training_sets import (
    create_training_set_version,
    get_training_set,
    list_training_sets
)
import pandas as pd

# Create training set version from DataFrame
df = pd.DataFrame({
    "id": [1, 2, 3],
    "feature_a": [0.1, 0.2, 0.3],
    "label": [1, 0, 1]
})

version = create_training_set_version(
    training_set_name="customer-churn",
    df=df,
    key_columns=["id"],
    description="Initial training data"
)

# Get training set
ts = get_training_set("customer-churn")

# List all training sets
all_sets = list_training_sets()

Vector Database (Pinecone)

from domino_data.vectordb import (
    domino_pinecone3x_init_params,
    domino_pinecone3x_index_params
)
from pinecone import Pinecone

# Initialize Pinecone client with Domino credentials
init_params = domino_pinecone3x_init_params("my-pinecone-ds")
pc = Pinecone(**init_params)

# Get index parameters
index_params = domino_pinecone3x_index_params("my-pinecone-ds", "embeddings")
index = pc.Index(**index_params)

# Query vectors
results = index.query(
    vector=[0.1, 0.2, 0.3, ...],
    top_k=10,
    include_metadata=True
)

Authentication

The library auto-configures authentication inside Domino workspaces and jobs using injected environment variables:

# Environment variables used automatically inside Domino:
# DOMINO_TOKEN_FILE - Token file location (preferred, short-lived)
# DOMINO_API_PROXY - API proxy URL
# DOMINO_DATA_API_GATEWAY - Data API gateway (default: http://127.0.0.1:8766)
#
# DOMINO_USER_API_KEY - Legacy API key (deprecated, will be removed)

For external use (e.g., CI/CD outside a Domino execution):

Note: DOMINO_USER_API_KEY is deprecated and will be removed in a future Domino release. Prefer running data-SDK code from inside a Domino workspace or job where token-based auth is injected automatically.

import os
os.environ["DOMINO_USER_API_KEY"] = "your-api-key"  # deprecated
os.environ["DOMINO_API_HOST"] = "https://your-domino.com"

from domino_data.data_sources import DataSourceClient
client = DataSourceClient()

Error Handling

from domino_data.data_sources import DominoError, UnauthenticatedError

try:
    result = ds.query("SELECT * FROM table")
except UnauthenticatedError:
    print("Authentication failed - check API key")
except DominoError as e:
    print(f"Domino error: {e}")

Best Practices

  1. Use within Domino: Auth is automatic in workspaces/jobs
  2. Parallel downloads: Use max_workers for large files
  3. Pagination: Use page_size when listing many objects
  4. Training Sets: Version your training data for reproducibility
  5. Connection reuse: Reuse client instances when possible

Package Info

  • PyPI: dominodatalab-data
  • GitHub: https://github.com/dominodatalab/domino-data
  • License: Apache 2.0
  • Python: 3.8+
Repository
dominodatalab/domino-claude-plugin
Commit

47c6e0a

Last updated
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