Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/palantir-local-dev-loop/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill palantir-local-dev-loop. Copyright stays with the author (MIT).

Palantir Local Dev Loop

Overview

Set up local development for Palantir Foundry integrations. Covers running transforms locally against sample data, mocking the Foundry API for fast iteration, and testing with pytest before pushing to Foundry.

Prerequisites

• Completed palantir-install-auth setup
• Python 3.9+ with pip
• A Foundry Code Repository cloned locally (or a standalone project)

Instructions

Step 1: Project Structure

my-foundry-project/
├── src/myproject/
│   ├── __init__.py
│   ├── pipeline.py          # @transform functions
│   └── utils.py             # Shared logic
├── tests/
│   ├── conftest.py           # Fixtures with sample DataFrames
│   ├── test_pipeline.py      # Transform unit tests
│   └── sample_data/          # CSV/Parquet test fixtures
├── .env                      # FOUNDRY_HOSTNAME, FOUNDRY_TOKEN
├── requirements.txt          # foundry-platform-sdk, pytest, pyspark
└── pyproject.toml

Step 2: Install Local Dependencies

set -euo pipefail
pip install foundry-platform-sdk pyspark pytest pandas
python -c "import foundry; import pyspark; print('Dependencies ready')"

Step 3: Test Transforms Locally with PySpark

# tests/conftest.py
import pytest
from pyspark.sql import SparkSession

@pytest.fixture(scope="session")
def spark():
    return SparkSession.builder.master("local[2]").appName("test").getOrCreate()

@pytest.fixture
def sample_orders(spark):
    data = [
        ("ORD-001", "alice@company.com", "2026-03-01", 99.99),
        ("ORD-002", "bob@test.com", "2026-03-02", 49.99),      # test email
        (None, "carol@company.com", "2026-03-03", 149.99),       # null ID
    ]
    return spark.createDataFrame(data, ["order_id", "email", "order_date_str", "total"])

# tests/test_pipeline.py
from myproject.pipeline import clean_orders

def test_clean_orders_removes_nulls_and_test_emails(sample_orders):
    result = clean_orders(sample_orders)
    assert result.count() == 1  # Only alice remains
    assert result.columns == ["order_id", "email", "order_date", "total_cents"]
    row = result.first()
    assert row.total_cents == 9999  # 99.99 * 100

Step 4: Mock Foundry API for Integration Tests

# tests/test_api.py
import pytest
from unittest.mock import MagicMock, patch

def test_list_ontology_objects():
    mock_client = MagicMock()
    mock_client.ontologies.OntologyObject.list.return_value.data = [
        MagicMock(properties={"fullName": "Alice", "department": "Engineering"}),
    ]

    result = mock_client.ontologies.OntologyObject.list(
        ontology="test", object_type="Employee", page_size=10
    )
    assert len(result.data) == 1
    assert result.data[0].properties["fullName"] == "Alice"

Step 5: Run Tests

set -euo pipefail
pytest tests/ -v --tb=short
# Expected: all tests pass against local Spark + mocked API

Step 6: Live API Smoke Test (Optional)

# scripts/smoke_test.py — runs against real Foundry (needs credentials)
import os, foundry, sys

client = foundry.FoundryClient(
    auth=foundry.UserTokenAuth(
        hostname=os.environ["FOUNDRY_HOSTNAME"],
        token=os.environ["FOUNDRY_TOKEN"],
    ),
    hostname=os.environ["FOUNDRY_HOSTNAME"],
)

try:
    ontologies = list(client.ontologies.Ontology.list())
    print(f"Smoke test passed: {len(ontologies)} ontologies accessible")
except foundry.ApiError as e:
    print(f"Smoke test failed: {e.status_code} {e.message}", file=sys.stderr)
    sys.exit(1)

Output

• Local PySpark environment for testing transforms without Foundry
• Mocked Foundry API client for integration tests
• pytest suite validating pipeline logic
• Optional live smoke test for credential verification

Error Handling

Examples

Watch Mode with pytest-watch

pip install pytest-watch
ptw tests/ -- -v --tb=short
# Re-runs tests on every file save

Resources

• Foundry Local Development
• Code Examples
• PySpark Testing

Next Steps

• Apply SDK patterns: palantir-sdk-patterns
• Build data pipelines: palantir-core-workflow-a