Ad Tech Data Pipelines
One Source of Truth for Reporting

Every platform reports in its own way. A DSP, an SSP and an ad server may name the same campaign differently, count impressions at different moments, and report in different time zones and currencies. Teams bridge these gaps by hand, usually under time pressure, exporting files and rebuilding the same spreadsheet each week. That works until someone is away or a column changes without warning, and nobody can say which numbers are still right.

A data pipeline moves that work into a repeatable process. Data is pulled on a schedule through APIs, stored in a warehouse and transformed once by agreed rules. The same logic then feeds every dashboard, so a question about spend, delivery or yield has one answer. This sits alongside our analytics and reporting services, which covers the reporting side of the same work, from KPI definitions to client-ready views.

Most major DSPs, SSPs and ad servers offer reporting APIs, log-level data files, or both. Their limits differ: some restrict date ranges, some delay data, and some change fields without much notice. We design connectors that handle paging, retries and late-arriving data, and we keep raw copies so numbers can always be traced back to the source when a number is questioned, which is often the first thing finance or a client asks.

In the warehouse, a modeling layer maps raw fields to shared definitions. Placement names are parsed, currencies converted with documented rates, and dates aligned to one time zone. Both BigQuery and Snowflake work well for this, and we use whichever your team already has, so no new vendor is needed. For more on the wider service area, see ad tech services. Our team handles the connectors and the monitoring, so failures are found by us before they reach a report.

A dashboard earns trust when its numbers match what the team sees in each platform, within explained limits. We build reconciliation views first, comparing platform-reported figures with ad server counts, then layer delivery, pacing and revenue views on top. Each metric has a written definition visible in the dashboard, so readers do not have to guess what a number includes.

Agencies can use white-label views for clients, and publishers can build internal views for sales and yield teams. We choose the tool you already use, such as Looker Studio, Looker, Tableau or Power BI. When you also want advice on the setup itself, our consulting and technical advisory team can help you decide what to build first and which views can wait.

This is an example, not a client result. A publisher compares a month of impressions for one campaign across two sources. The ad server reports 1,000,000 and the DSP reports 970,000, a gap of 3 percent.

The pipeline stores both numbers side by side, each with its source, time zone and definition. A rule set in advance by the team decides what range is normal for that pair and what triggers a review. If the gap exceeds the range, the team checks for late-arriving data, time-zone cutoffs, invalid-traffic filtering or counting method. The point is that differences are visible and explained, not hidden.

The first phase defines the data before any code runs. You receive:

• A list of sources and the API or export used for each, such as the GAM reporting API or DV360 reporting.

• A data dictionary of metrics, names and time zones, with definitions agreed.

• A schema for the warehouse tables.

• A reconciliation plan listing which sources will be compared and the review ranges you set.

Look at these: weekly exports are copied into spreadsheets; campaign names differ between platforms; currency or time zone is fixed by hand; numbers change after the report was sent; and nobody can say which source is right.

If you see two or three of these, automated feeds are likely to save time. If your data volume is small and reporting is stable, a lighter approach may be enough.