How it works · attribution that keeps pace with spend
Impressions, clicks, installs and in-app events arrive across a dozen sources at terabyte volume. Most attribution pipelines start fast and quietly degrade: bad partitioning, lazy-evaluation surprises, uncached joins. We build and tune the pipeline layer so ROAS attribution stays real-time as spend scales, not months behind it.
What it does · three capabilities
We find where Spark jobs actually lose time: lazy evaluation, shuffle, bad partition strategy, the failure modes that don't show up in standard monitoring.
GCS-to-Iceberg and equivalent pipelines rebuilt to hold correct execution plans under real production load, not just in a staging environment.
ROAS and user-acquisition attribution that reports at the latency marketing teams actually need to make spend decisions same-day.
Proof over promises
Real-time ROAS (Return on Ad Spend) engine processing terabyte-scale user acquisition data. Built on AWS with Apache Spark, handling GCS-to-Iceberg ETL pipelines.
TB+
Data processed daily
Real-time
Attribution latency
Most degraded Spark pipelines have a specific, findable root cause. We'll audit yours and tell you what we find.