The Problem
A leading German automotive OEM processed millions of vehicle test records daily through outdated Airflow infrastructure. The system was too slow, couldn't scale to petabyte level, and delivered inconsistent data quality due to missing ACID transactions. Test data arrived too late at analysis teams — decisions were made on stale data.
The Solution
We replaced the legacy Airflow system with a custom Kubernetes-native scheduler (AKS) that orchestrates PySpark jobs directly on the cluster — no external scheduler overhead.
The data layer was migrated to Delta Lake with Data Vault 2.0 architecture: ACID transactions, time-travel for audit trails, and incremental processing instead of full reload cycles.
The result: a pipeline that processes millions of vehicle test records at petabyte scale while guaranteeing consistent, auditable data quality to Data Vault 2.0 standards.
The Results
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