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

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:

# Environment variables used automatically:
# DOMINO_USER_API_KEY - API key for authentication
# DOMINO_TOKEN_FILE - Token file location
# DOMINO_API_PROXY - API proxy URL
# DOMINO_DATA_API_GATEWAY - Data API gateway (default: http://127.0.0.1:8766)

For external use:

import os
os.environ["DOMINO_USER_API_KEY"] = "your-api-key"
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+