Wialon to PostgreSQL Data Pipeline

Every fleet analytics effort starts the same way: someone runs a Wialon report, exports it to CSV, reshapes it in a spreadsheet, and emails a chart. It works until the follow-up question arrives. Then the report is run again with slightly different parameters, re-exported, re-transformed, and re-emailed — and the two versions no longer agree, because the transformation lived in one person's formulas rather than anywhere reviewable.

The cost is rarely counted honestly. In fleets of a few hundred vehicles this cycle routinely consumes several working days a month, produces numbers nobody can reproduce six weeks later, and blocks any analysis that needs history longer than the report window. A pipeline replaces that with a table analysts can query, and with logic that lives in version control instead of in a workbook.

Report data is pulled on a schedule, deduplicated against what already landed, transformed into a stable shape, and written into PostgreSQL with a run record attached. Every table carries the run that produced it, so a bad load can be identified and replaced rather than argued about.

Wialon stays the system for what is happening right now: live positions, active alerts, dispatch state. Those questions need sub-second latency against the source, and reproducing them in a warehouse adds complexity while always lagging behind.

The warehouse answers what happened — cost trends over six months, utilisation for next year's fleet sizing, profitability per customer once empty running is counted. Those tolerate minutes or hours of latency because the decisions they inform are weekly or monthly. Keeping the two apart is what stops analytical aggregations from competing with live queries for the same resources, and gives everyone a simple routing rule: right now goes to Wialon, last month goes to SQL.

Source retention, not warehouse capacity, sets how much history you can ever have. Once a period ages out of the source, it is gone — no pipeline can recover it later. That makes the first pull a decision with a deadline: whatever is reachable on day one is the deepest history the warehouse will ever hold.

So the first thing we establish is what your deployment actually retains, and the first thing we run is the deepest backfill that retention allows — before building anything downstream. Teams that postpone the pipeline for a quarter usually lose a quarter of history they cannot buy back. Where a fleet needs richer raw telemetry than Wialon retains, a dual-stack setup is the usual answer.