The challenge
An existing Adobe Analytics to Snowflake pipeline that had been built by three different teams over four years. No documentation. Inconsistent field naming. Duplicate records in some tables, missing data in others. The data science team had stopped using the pipeline and was re-pulling data manually from the Adobe Analytics API.
We began with a full audit of the existing pipeline — tracing data from Adobe Analytics feed files through transformation logic to final Snowflake tables. We documented every transformation, identified every inconsistency, and mapped every downstream consumer.
The redesign principles
The new pipeline was designed around three principles: transparency, consistency, and governance. Transparency meant every transformation was documented and testable. Consistency meant field names and data types were standardized across all tables. Governance meant every change to the pipeline required a documented rationale and a validation step.
Technical implementation
We redesigned the pipeline using a modular architecture that separated raw ingestion, transformation, and serving layers. Adobe Analytics data feeds were ingested into a raw layer without transformation. A documented transformation layer produced clean, typed, deduplicated tables. A serving layer exposed curated views for specific use cases — BI reporting, data science, and marketing analytics.
We also implemented data quality monitoring — automated checks that ran after each pipeline execution and alerted the analytics team to anomalies before they reached downstream consumers.
Outcome
Pipeline reliability reached 100% over the first three months post-launch. Manual data pulls from the Adobe Analytics API decreased by 60% as the data science team returned to using the pipeline. The data quality monitoring caught three significant data anomalies in the first quarter that would previously have gone undetected.