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navan-data-handling Extract and transform Navan booking and transaction data using pagination, filtering, and data pipeline connectors.
Use when building data warehouses, analytics dashboards, or debugging data quality issues with Navan data.
Trigger with "navan data handling", "navan data extraction", "navan pagination".
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jeremylongshore
jeremylongshore/tons-of-skills-marketplace
GitHub 저장소 열기 name navan-data-handling description Extract and transform Navan booking and transaction data using pagination, filtering, and data pipeline connectors.
Use when building data warehouses, analytics dashboards, or debugging data quality issues with Navan data.
Trigger with "navan data handling", "navan data extraction", "navan pagination".
allowed-tools Read, Write, Edit, Bash(npm:*), Bash(curl:*), Bash(pip:*), Grep version 1.7.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","navan","travel"] compatibility Designed for Claude Code
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 ${access_token} ` };
async
function
extractAllBookings
startDate : string , endDate : string
const
allBookings
any
let
0
const
50
while
true
const
await
fetch
`${process.env.NAVAN_BASE_URL} /v1/bookings`
`?createdFrom=${startDate} &createdTo=${endDate} `
`&page=${page} &size=${size} `
if
status
429
const
parseInt
headers
get
'Retry-After'
'5'
await
new
Promise
r =>
setTimeout
1000
continue
const
await
json
if
length
break
push
if
length
break
console
log
`Fetched ${allBookings.length} bookings...`
return
const
await
extractAllBookings
'2026-01-01'
'2026-03-31'
console
log
`Total bookings extracted: ${bookings.length} `
Step 2: UUID-Based Deduplication
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);
}
}
return Array .from (seen.values ());
}
const deduplicated = deduplicateByUUID (trips);
console .log (`After dedup: ${deduplicated.length} unique trips (was ${trips.length} )` );
Step 3: Date-Range Filtering and Chunking
function * dateChunks (start : string , end : string , daysPerChunk : number const startDate = new Date (start);
const endDate = new Date (end);
while (startDate < endDate) {
const chunkEnd = new Date (startDate);
chunkEnd.setDate (chunkEnd.getDate () + daysPerChunk);
if (chunkEnd > endDate) chunkEnd.setTime (endDate.getTime ());
yield {
start : startDate.toISOString ().split ('T' )[0 ],
end : chunkEnd.toISOString ().split ('T' )[0 ],
};
startDate.setDate (startDate.getDate () + daysPerChunk + 1 );
}
}
for (const chunk of dateChunks ('2025-01-01' , '2026-03-31' , 30 )) {
const chunkBookings = await extractAllBookings (chunk.start , chunk.end );
console .log (`${chunk.start} to ${chunk.end} : ${chunkBookings.length} bookings` );
}
Step 4: Fivetran Connector Setup Configure Fivetran for automated data extraction:
In Fivetran dashboard, add a new connector and search for "Navan"
Enter your OAuth credentials (client_id, client_secret)
Select destination warehouse (Snowflake, BigQuery, Redshift)
Configure sync frequency (recommended: daily for BOOKING, hourly for TRANSACTION)
Map schema: Fivetran creates navan.booking and navan.transaction tables
SELECT
department,
COUNT (* ) AS trip_count,
SUM (total_cost) AS total_spend,
AVG (total_cost) AS avg_trip_cost
FROM navan.booking
WHERE booking_date >= '2026-01-01'
GROUP BY department
ORDER BY total_spend DESC ;
Step 5: Airbyte Connector Configuration
sourceDefinitionId: source-navan
connectionConfiguration:
client_id: "${NAVAN_CLIENT_ID}"
client_secret: "${NAVAN_CLIENT_SECRET}"
In Airbyte, add source > search "Navan"
Enter client_id and client_secret
Select "bookings" stream
Set sync mode to "Full Refresh | Overwrite" (matches Navan's weekly re-import)
Configure destination and schedule
Step 6: Schema Mapping for Analytics
interface NormalizedBooking {
booking_id : string ;
employee_email : string ;
department : string ;
cost_center : string ;
origin : string ;
destination : string ;
start_date : string ;
end_date : string ;
booking_type : string ;
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 Error HTTP Code Cause Solution Unauthorized 401 Expired or invalid bearer token Re-authenticate via POST /ta-auth/oauth/token Forbidden 403 Insufficient API scope for admin endpoints Verify admin-level credentials Rate Limited 429 Too many API requests Use exponential backoff; chunk date ranges Timeout 504 Date range too large Split into 30-day chunks Empty Response 200 No data in date range Verify date format (YYYY-MM-DD); widen range Connector Auth Failed N/A Invalid credentials in Fivetran/Airbyte Verify client_id and client_secret
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' )
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
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.
