Cloud migrations at scale are rarely about technology alone. When a Fortune 500 financial services firm decided to move their entire data platform from Azure to GCP, the challenge was not just technical — it was organizational. 200+ data pipelines, 15 engineering teams, and a hard deadline of 6 months with zero tolerance for data loss or extended downtime.
Why the Migration?
The client had accumulated significant Azure Data Factory and Azure Synapse infrastructure over 5 years. With a new enterprise agreement favoring GCP and a strategic decision to standardize on BigQuery for analytics, the migration was approved at the executive level. The business constraint was clear: no interruption to daily reporting that feeds board-level dashboards.
Our Methodology: Parallel Run Architecture
We adopted a parallel run approach rather than a hard cutover. For 8 weeks, both Azure and GCP pipelines ran simultaneously, with automated reconciliation jobs comparing row counts and aggregate values across systems. This let us catch discrepancies in business logic translation — particularly in time zone handling and currency conversion — before any traffic was shifted.
Technical Highlights
We migrated Azure Data Factory pipelines to Cloud Composer (managed Airflow), translating ADF's visual pipeline definitions into Airflow DAGs programmatically using a custom parser. Azure Synapse T-SQL was converted to BigQuery Standard SQL with an automated tool that handled ~80% of transformations, leaving the remaining 20% requiring manual review — primarily window functions and stored procedures.
Lessons Learned
The most valuable lesson: invest in a metadata-driven approach from day one. By treating pipeline definitions as data, we enabled automated translation, automated testing, and rollback capability. Teams that resisted this structure spent 3× more time in the manual conversion phase.