Navan Data Handling

Overview

This skill covers data extraction and transformation patterns for Navan booking and transaction data. Navan exposes two primary data tables with different refresh behaviors: BOOKING (full re-import weekly, keyed by UUID) and TRANSACTION (incremental append-only). Data can be extracted via the direct REST API or through managed connectors — Fivetran, Airbyte (source-navan v0.0.42), and Estuary Flow. This skill provides pagination patterns, date-range filtering, UUID-based deduplication, and schema mapping for downstream analytics.

Prerequisites

• Navan account with OAuth 2.0 API credentials (see navan-install-auth)
• For direct API: Node.js 18+ or Python 3.8+
• For Fivetran: Fivetran account with Navan connector
• For Airbyte: Airbyte instance (Cloud or OSS) with source-navan v0.0.42+
• Environment variables: NAVAN_CLIENT_ID, NAVAN_CLIENT_SECRET, NAVAN_BASE_URL

Instructions

Step 1: Direct API — Paginated Booking Extraction

const tokenRes = await fetch(`${process.env.NAVAN_BASE_URL}/ta-auth/oauth/token`, {
  method: 'POST',
  headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
  body: new URLSearchParams({
    grant_type: 'client_credentials',
    client_id: process.env.NAVAN_CLIENT_ID!,
    client_secret: process.env.NAVAN_CLIENT_SECRET!,
  }),
});
const { access_token } = await tokenRes.json();
const headers = { Authorization: `Bearer ${access_token}` };

// Paginate through all bookings using page + size params
async function extractAllBookings(startDate: string, endDate: string) {
  const allBookings: any[] = [];
  let page = 0;
  const size = 50;

  while (true) {
    const res = await fetch(
      `${process.env.NAVAN_BASE_URL}/v1/bookings` +
      `?createdFrom=${startDate}&createdTo=${endDate}` +
      `&page=${page}&size=${size}`,
      { headers }
    );

    if (res.status === 429) {
      // Rate limited — exponential backoff
      const retryAfter = parseInt(res.headers.get('Retry-After') ?? '5');
      await new Promise(r => setTimeout(r, retryAfter * 1000));
      continue;
    }

    const { data } = await res.json();
    if (!data || !data.length) break;

    allBookings.push(...data);
    if (data.length < size) break; // last page
    page++;
    console.log(`Fetched ${allBookings.length} bookings...`);
  }
  return allBookings;
}

const bookings = await extractAllBookings('2026-01-01', '2026-03-31');
console.log(`Total bookings extracted: ${bookings.length}`);

Step 2: UUID-Based Deduplication

// BOOKING table re-imports weekly — same UUID may appear in multiple extractions
function deduplicateByUUID(records: any[]): any[] {
  const seen = new Map<string, any>();
  for (const record of records) {
    const existing = seen.get(record.uuid);
    if (!existing || record.updated_at > existing.updated_at) {
      seen.set(record.uuid, record);  // keep newest version
    }
  }
  return Array.from(seen.values());
}

const deduplicated = deduplicateByUUID(trips);
console.log(`After dedup: ${deduplicated.length} unique trips (was ${trips.length})`);

Step 3: Date-Range Filtering and Chunking

// Split large date ranges into chunks to avoid timeouts
function* dateChunks(start: string, end: string, daysPerChunk: number) {
  const startDate = new Date(start);
  const endDate = new Date(end);

  while (startDate < endDate) {
    const chunkEnd = new Date(startDate);
    chunkEnd.setDate(chunkEnd.getDate() + daysPerChunk);
    if (chunkEnd > endDate) chunkEnd.setTime(endDate.getTime());

    yield {
      start: startDate.toISOString().split('T')[0],
      end: chunkEnd.toISOString().split('T')[0],
    };
    startDate.setDate(startDate.getDate() + daysPerChunk + 1);
  }
}

// Extract in 30-day chunks
for (const chunk of dateChunks('2025-01-01', '2026-03-31', 30)) {
  const chunkBookings = await extractAllBookings(chunk.start, chunk.end);
  console.log(`${chunk.start} to ${chunk.end}: ${chunkBookings.length} bookings`);
}

Step 4: Fivetran Connector Setup

Configure Fivetran for automated data extraction:

1. In Fivetran dashboard, add a new connector and search for "Navan"
2. Enter your OAuth credentials (clientid, clientsecret)
3. Select destination warehouse (Snowflake, BigQuery, Redshift)
4. Configure sync frequency (recommended: daily for BOOKING, hourly for TRANSACTION)
5. Map schema: Fivetran creates navan.booking and navan.transaction tables

-- Fivetran destination query: trip summary by department
SELECT
  department,
  COUNT(*) AS trip_count,
  SUM(total_cost) AS total_spend,
  AVG(total_cost) AS avg_trip_cost
FROM navan.booking
WHERE booking_date >= '2026-01-01'
GROUP BY department
ORDER BY total_spend DESC;

Step 5: Airbyte Connector Configuration

# Airbyte source-navan connector config (v0.0.42)
# Supports one stream: bookings
sourceDefinitionId: source-navan
connectionConfiguration:
  client_id: "${NAVAN_CLIENT_ID}"
  client_secret: "${NAVAN_CLIENT_SECRET}"
  # Available streams: bookings
  # Sync mode: full_refresh (BOOKING table is re-imported weekly)

Airbyte setup steps:

1. In Airbyte, add source > search "Navan"
2. Enter clientid and clientsecret
3. Select "bookings" stream
4. Set sync mode to "Full Refresh | Overwrite" (matches Navan's weekly re-import)
5. Configure destination and schedule

Step 6: Schema Mapping for Analytics

// Map Navan API response fields to analytics schema
interface NormalizedBooking {
  booking_id: string;     // from uuid
  employee_email: string;
  department: string;
  cost_center: string;
  origin: string;
  destination: string;
  start_date: string;
  end_date: string;
  booking_type: string;   // flight, hotel, car
  total_cost: number;
  currency: string;
  policy_compliant: boolean;
  created_at: string;
  updated_at: string;
}

function normalizeBooking(raw: any): NormalizedBooking {
  return {
    booking_id: raw.uuid,
    employee_email: raw.traveler_email ?? raw.email,
    department: raw.department ?? 'Unknown',
    cost_center: raw.cost_center ?? '',
    origin: raw.origin,
    destination: raw.destination,
    start_date: raw.start_date,
    end_date: raw.end_date,
    booking_type: raw.type ?? 'flight',
    total_cost: parseFloat(raw.total_cost ?? raw.amount ?? '0'),
    currency: raw.currency ?? 'USD',
    policy_compliant: raw.in_policy ?? true,
    created_at: raw.created_at,
    updated_at: raw.updated_at,
  };
}

Output

Successful execution produces:

• Paginated trip and transaction records extracted via REST API
• Deduplicated records keyed by UUID for the BOOKING table
• Configured Fivetran or Airbyte connectors for automated extraction
• Normalized schema mappings ready for warehouse loading

Error Handling

Examples

Python — Bulk extraction with retry logic:

import requests
import time
import os

base_url = os.environ.get('NAVAN_BASE_URL', 'https://api.navan.com')
auth = requests.post(f'{base_url}/ta-auth/oauth/token', data={
    'grant_type': 'client_credentials',
    'client_id': os.environ['NAVAN_CLIENT_ID'],
    'client_secret': os.environ['NAVAN_CLIENT_SECRET'],
})
headers = {'Authorization': f'Bearer {auth.json()["access_token"]}'}

def extract_with_retry(endpoint, params, max_retries=3):
    for attempt in range(max_retries):
        res = requests.get(f'{base_url}/{endpoint}', params=params, headers=headers)
        if res.status_code == 200:
            return res.json()
        elif res.status_code == 429:
            wait = int(res.headers.get('Retry-After', 2 ** attempt))
            print(f'Rate limited, waiting {wait}s...')
            time.sleep(wait)
        else:
            res.raise_for_status()
    raise Exception(f'Failed after {max_retries} retries')

# Records in .data array, paginated with page + size
resp = extract_with_retry('v1/bookings', {
    'createdFrom': '2026-01-01', 'createdTo': '2026-03-31', 'page': 0, 'size': 50
})
bookings = resp['data']
print(f'Extracted {len(bookings)} bookings')

Resources

• Navan Help Center — Official API documentation
• Booking Data Integration — Booking data export and connector setup
• Navan Integrations — Fivetran, Airbyte, and Estuary connectors

Next Steps

After setting up data extraction, proceed to navan-data-sync for incremental sync strategies or navan-performance-tuning for optimizing large data pulls.