palantir-migration-deep-dive

name	palantir-migration-deep-dive
description	Execute major Palantir Foundry migration strategies including data migration, API version upgrades, and platform transitions. Use when migrating data into Foundry, upgrading between API versions, or re-platforming existing integrations. Trigger with phrases like "migrate to palantir", "foundry migration", "palantir data migration", "foundry replatform".
allowed-tools	Read, Write, Edit, Bash(pip:*), Bash(node:*)
version	1.5.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","palantir","foundry","migration","data-migration"]
compatibility	Designed for Claude Code

Palantir Migration Deep Dive

Overview

Comprehensive guide for migrating data into Foundry, migrating from legacy systems to Foundry-backed architectures, and upgrading between Foundry API versions using the strangler fig pattern.

Prerequisites

• Source system access and schema documentation
• Foundry enrollment with write access
• Understanding of Foundry data pipeline architecture (palantir-reference-architecture)

Instructions

Step 1: Migration Assessment

## Migration Checklist
- [ ] Source system inventory (tables, volumes, refresh rates)
- [ ] Data classification (PII, confidential, public)
- [ ] Schema mapping: source columns → Foundry dataset columns
- [ ] Volume estimate: rows, GB, growth rate
- [ ] Dependencies: downstream consumers of source data
- [ ] Timeline: parallel run period, cutover date

Step 2: Data Migration — Bulk Import

import foundry, pandas as pd

client = get_foundry_client()

# Read source data (example: PostgreSQL)
df = pd.read_sql("SELECT * FROM orders WHERE year >= 2024", source_conn)

# Upload to Foundry dataset
client.datasets.Dataset.upload(
    dataset_rid="ri.foundry.main.dataset.xxxxx",
    branch_id="master",
    file_path="orders.parquet",
    data=df.to_parquet(),
    content_type="application/x-parquet",
)
print(f"Uploaded {len(df)} rows to Foundry")

Step 3: Incremental Sync (Ongoing)

from datetime import datetime, timedelta

def incremental_sync(client, source_conn, dataset_rid, last_sync):
    """Sync only new/changed rows since last sync."""
    query = 
    df = pd.read_sql(query, source_conn)
     df.empty:
        ()
         last_sync

    client.datasets.Dataset.upload(
        dataset_rid=dataset_rid,
        branch_id=,
        file_path=,
        data=df.to_parquet(),
    )
    ()
     df[].()