Manhattan Associates powers over a billion daily API calls with Google Cloud databases

Manhattan Associates' migration from Oracle and DB2 to Google Cloud SQL handling over a billion daily API calls signals a critical shift in how we architect stateful services at scale. What strikes me is that this isn't primarily a cost story—it's about operational velocity. Legacy database platforms demanded dedicated DBAs and rigid capacity planning; Cloud SQL abstracts that overhead while providing built-in replication, backup, and monitoring. For data engineering teams, this means we can finally stop treating database infrastructure as a bottleneck and focus on query optimization and schema evolution instead of patch management. The architecture pattern here—decoupling transaction processing from analytical workloads through cloud-native databases—is becoming table stakes. My recommendation: audit your legacy database dependencies now. If you're still running on-premises Oracle for transactional workloads while trying to build modern analytics pipelines, you're fragmenting your operational burden. Cloud SQL's integration with BigQuery for analytical queries and its native support for read replicas lets you implement the separation of concerns that modern data stacks demand, without the architectural gymnastics required five years ago.