Palantir Local Dev Loop Skill

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

| Error | Cause | Solution | |-------|-------|----------| | Java not found (PySpark) | JDK not installed | Install JDK 11+: apt install openjdk-11-jdk | | ModuleNotFoundError: pyspark | Missing dependency | pip install pyspark | | Import error on transform functions | Circular imports | Keep transforms in separate modules | | Spark AnalysisException | Column name mismatch | Print df.columns in test to debug |

Examples

Watch Mode with pytest-watch

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

Resources

Next Steps

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

Agent Skills: Palantir Local Dev Loop

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Skill Files