Your stack

Everything that speaks PostgreSQL already speaks FleetSQL

There is no FleetSQL API to integrate against, and that is the feature. Your fleet data lands in a standard PostgreSQL database, so the tools your team already runs connect with a host, a port and a password — no connector to build, no vendor SDK to track.

Protocol
Standard PostgreSQL wire
Setup
A connection string
Drivers
Every language has one
Lock-in
None — it is your database

Dashboards and BI

Metabase, Grafana, Power BI, Tableau, Superset, Looker and every other BI tool ships a PostgreSQL connector, so the integration is a form with five fields rather than a project. Point the tool at the database, build against wialon_standard and the event tables, and refresh on whatever schedule the tool already uses.

Because the data is a database rather than a report export, drill-down works the way analysts expect: a chart of idle time by month clicks through to the vehicle, then to the day, then to the individual eco-driving events — without anyone re-running an extraction to answer the follow-up question.

  • Grafana — operational panels and threshold alerting straight off SQL queries
  • Metabase / Superset — self-service questions for people who do not write SQL
  • Power BI / Tableau / Looker — where fleet data joins the rest of the business
  • DBeaver / psql — for the person who just wants to check a number

Notebooks, Python and machine learning

History in a database is what makes forecasting and anomaly detection possible at all — a report export is a snapshot, and a model needs a series. With up to three years backfilled on the first run, a notebook has something to learn from on day one.

Pull a fleet-wide daily series into pandas 
import pandas as pd
from sqlalchemy import create_engine

engine = create_engine("postgresql://user:password@host:5432/fleet")

daily = pd.read_sql("""
    SELECT date, unit_id, trips_km, engine_hours, idling_hours,
           consumed_fls_liters
    FROM   wialon_standard
    WHERE  date >= CURRENT_DATE - INTERVAL '2 years'
""", engine, parse_dates=["date"])

daily["l_per_100km"] = 100 * daily.consumed_fls_liters / daily.trips_km

AI agents and text-to-SQL

Every serious agent framework and MCP server speaks PostgreSQL, which makes a FleetSQL database directly askable in plain language: "which trucks idled more than three hours yesterday" becomes a query the agent writes, runs and explains. No custom tool wrapper, no scraping a reporting UI.

The practical requirement is a schema an agent can reason about, which is why the tables are few, named plainly and documented in full on the data model page. A wide, opaque schema is what makes text-to-SQL hallucinate; a narrow documented one is what makes it useful.

Automation and alerting

n8n, Zapier, Airflow, cron — anything that can run a query on a schedule can turn a row into an action. A drain over twenty litres becomes a webhook into your ticketing system; a vehicle whose last_message_time has gone quiet becomes a maintenance task; a driver crossing a violation threshold becomes a line in the weekly review.

Because the alert logic lives in SQL rather than inside a telematics platform's notification builder, it is reviewable, version-controllable and testable against history — you can run this month's rule against last year to see how often it would have fired.

Joining the rest of the business

The highest-value integration is usually not a tool at all: it is the table you create next to ours. Fuel card transactions, customer contracts, ERP cost centres, maintenance records — once they sit in the same database as the telemetry, questions that used to require three exports and a spreadsheet become one query.

That is also the point at which fleet data starts answering commercial questions rather than operational ones: cost per delivery, profitability per customer, the real cost of an empty return leg. Asset Track builds those joins as a service when the sources are messier than a CSV — see Wialon integration services.

Frequently asked questions

Do we need a special driver or connector? +

No. It is a standard PostgreSQL database, so whatever PostgreSQL driver your language or BI tool already ships is the integration. There is no FleetSQL client library, and nothing to update when we release.

Can we connect an LLM agent or MCP server to it? +

Yes, and it works better than pointing one at a telematics API, because the schema is small and documented. Agents fail on wide, cryptic schemas; a handful of plainly named tables with published columns is close to the best case for text-to-SQL.

Is there a read-only user for BI tools? +

Yes. Dashboards and agents should connect with a read-only role rather than the credentials your own jobs use, so a misbehaving query or a curious analyst cannot modify anything. Credentials are issued per integration, so one can be revoked without disturbing the rest.

Can we replicate the database into our own warehouse? +

Yes. Standard PostgreSQL replication and ETL tools all work, and teams that already run Snowflake or BigQuery usually schedule a copy rather than pointing dashboards at the operational database. Nothing about the setup prevents it.

Worth reading

Why and how to extract your Wialon fleet data

Four reasons to get telematics data out of Wialon — and three ways to do it: by hand, a custom ETL pipeline, or FleetSQL straight to PostgreSQL.

Wialon data extraction: working within the limits

How to pull years of fleet history out of Wialon Hosting without hitting its rate limits, concurrency caps, or report timeouts.

Contact

Interested in FleetSQL?

Why FleetSQL

Your Telematics data in a PostgreSQL database you control — queryable, joinable, and yours.