Enterprise teams that consume flight data usually want it in the same place they watch everything else — Splunk, Grafana, Datadog, a Power BI report, whatever their ops team already has open all day. Nobody wants another tab. The problem is that flight data APIs return JSON over REST, and dashboarding tools expect time-series metrics in a data store they already know how to query. This post covers the plumbing between the two.
We'll focus on Splunk and Grafana because they're what we see most often, but the pattern is the same for any tool that can read from a database or receive structured logs.
The general pattern
The ingestion side is always the same three steps:
- A poller calls the API on a schedule — every 10 seconds for live position data, every few minutes for status or weather.
- A writer puts the result into whatever store the dashboard reads from — Splunk's HTTP Event Collector, a Postgres table that Grafana queries, an InfluxDB bucket, a Kafka topic.
- The dashboard queries that store with its native query language. No API calls from the dashboard itself.
The reason you don't call the API directly from the dashboard is rate limits and latency. A Grafana panel that fires an HTTP request to an external API on every refresh, for every user who has the dashboard open, will burn through your quota in hours and add unpredictable latency to every panel load. A poller that writes to a local store decouples the two problems.
Splunk integration
Splunk's natural ingestion path is the HTTP Event Collector (HEC). You POST JSON events to it, and they're immediately searchable.
A minimal poller that feeds aircraft positions into Splunk HEC:
import requests, time, json
BASE = "https://skylink-api.p.rapidapi.com"
H = {"X-RapidAPI-Key": KEY, "X-RapidAPI-Host": "skylink-api.p.rapidapi.com"}
SPLUNK_HEC = "https://splunk.internal:8088/services/collector/event"
SPLUNK_TOKEN = SPLUNK_HEC_TOKEN # from your Splunk admin
def poll_and_ingest(bbox, source_name):
r = requests.get(f"{BASE}/adsb/aircraft", headers=H,
params={"bbox": bbox}, timeout=15)
r.raise_for_status()
aircraft = r.json().get("aircraft", [])
for ac in aircraft:
event = {
"event": ac,
"source": source_name,
"sourcetype": "adsb_position",
"index": "flight_data",
}
requests.post(SPLUNK_HEC, json=event,
headers={"Authorization": f"Splunk {SPLUNK_TOKEN}"},
verify=True, timeout=10)
while True:
poll_and_ingest("40.4,-74.3,40.9,-73.7", "jfk_area")
time.sleep(10)Once the data is in Splunk, you search it with SPL:
index=flight_data sourcetype=adsb_position
| spath callsign | spath altitude | spath ground_speed
| where altitude < 5000
| stats count by callsign
| sort -countUseful Splunk panels for flight data:
- Aircraft count over time — a timechart of
| stats dc(icao24)gives you traffic volume trends. Sudden drops mean either fewer flights or a feed issue. - Staleness monitor — compute the age of each position record and alert if p95 exceeds your threshold. This catches feed degradation before it affects operations.
- Low-altitude alerts — aircraft below a configurable altitude in a bounding box. Useful for noise monitoring or approach-path analysis.
One thing to watch: ADS-B position data at 10-second polling intervals produces a lot of events. A single bounding box covering a busy terminal area might return 200 aircraft per poll, which is 1.7 million events per day for that one box. Size your Splunk license accordingly, or filter to only the fields and aircraft you actually need.
Grafana integration
Grafana doesn't ingest data — it reads from a data source. The most common pattern is writing to PostgreSQL with TimescaleDB, or InfluxDB, and pointing Grafana at it.
A poller that writes to Postgres:
import psycopg2, requests, time
BASE = "https://skylink-api.p.rapidapi.com"
H = {"X-RapidAPI-Key": KEY, "X-RapidAPI-Host": "skylink-api.p.rapidapi.com"}
conn = psycopg2.connect("dbname=flights user=grafana")
cur = conn.cursor()
def poll_and_store(bbox):
r = requests.get(f"{BASE}/adsb/aircraft", headers=H,
params={"bbox": bbox}, timeout=15)
r.raise_for_status()
for ac in r.json().get("aircraft", []):
cur.execute("""
INSERT INTO positions
(ts, icao24, callsign, lat, lon, altitude,
ground_speed, is_on_ground)
VALUES (NOW(), %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (icao24, ts) DO NOTHING
""", (
ac.get("icao24"), ac.get("callsign"),
ac.get("latitude"), ac.get("longitude"),
ac.get("altitude"), ac.get("ground_speed"),
ac.get("is_on_ground"),
))
conn.commit()
while True:
poll_and_store("40.4,-74.3,40.9,-73.7")
time.sleep(10)The Grafana panels that work well:
- Geomap panel — plot
lat/londirectly. Grafana's built-in map panel handles this without plugins. Colour the dots by altitude or speed. - Time-series count —
SELECT date_trunc('minute', ts), COUNT(DISTINCT icao24) FROM positions GROUP BY 1gives you a traffic-volume line. - Histogram of staleness — if you store
last_seenfrom the API response, you can compute and histogram the age of each record to monitor feed health. - Table of current ground traffic — filter
is_on_ground = truewith the latest timestamp pericao24. Operations teams use this as a quick heads-up display.
For InfluxDB, the write side is slightly different (you use the InfluxDB line protocol or client library) but the dashboard side is the same — point Grafana at the InfluxDB data source and write Flux queries.
Alerting
The dashboard is the passive view. The alerting is where you get operational value.
Alerts that teams actually find useful (as opposed to alerts that generate noise):
- Feed health: if the count of aircraft in your bounding box drops below a threshold for more than 2 consecutive polls, something is wrong — either with the feed or with your poller. Alert on this before users notice.
- Arrival deviation: if an expected inbound's ETA shifts by more than 15 minutes from the scheduled time. Use the flight time prediction endpoint and compare the predicted arrival to the schedule.
- Airspace incursion: aircraft appearing in a bounding box where you don't expect traffic. Some teams use this for security monitoring around sensitive sites.
In Grafana, these are configured as alert rules attached to the panel query with a threshold condition. In Splunk, they're saved searches with trigger actions. Neither tool needs anything special for flight data — the alerts work the same way they do for any other metric.
Monitoring the feed itself
One mistake we see: teams build dashboards on flight data and forget to monitor the data pipeline. The poller crashes, the database fills up, the API key expires — and the dashboard just shows stale data without any indication that it's stale.
Add a second data stream: poll the health endpoint (GET /adsb/health) every 60 seconds and write the result to the same store. This gives you an independent signal on feed status. If your aircraft-count panel shows zero but the health endpoint says the feed is fine, your poller is broken. If both show a problem, the feed is actually degraded.
# Health check — write alongside position data
health = requests.get(f"{BASE}/adsb/health", headers=H, timeout=10)
h = health.json()
print(f"Feed status: {h.get('status')} "
f"Aircraft tracked: {h.get('total_aircraft')}")Getting started
The free trial gives you 1,000 requests/month, which is enough to build a prototype dashboard polling one region at a low interval and verify that the data looks right in your tooling. For production dashboards polling continuously, you'll need a paid tier — the volume depends on how many regions you monitor and at what interval.
The API reference for the endpoints used here — ADS-B positions, health, flight time predictions — covers the full response schema so you can plan your database table or Splunk field extractions before writing code.